From 07cd4d83e1215d9cff6f9ccce9ac63fed3f6df7e Mon Sep 17 00:00:00 2001 From: Andrew Nguonly Date: Thu, 3 Jul 2025 16:04:27 -0700 Subject: [PATCH 01/31] docs: Add note about preemptive compute infra (#5339) * Add note about preemtive compute infra. * Fix spelling error. --- docs/docs/concepts/langgraph_control_plane.md | 33 +++++++++++++++---- 1 file changed, 26 insertions(+), 7 deletions(-) diff --git a/docs/docs/concepts/langgraph_control_plane.md b/docs/docs/concepts/langgraph_control_plane.md index f3562516c..ebc880ce5 100644 --- a/docs/docs/concepts/langgraph_control_plane.md +++ b/docs/docs/concepts/langgraph_control_plane.md @@ -46,21 +46,40 @@ For simplicity, the control plane offers two deployment types with different res | **Deployment Type** | **CPU/Memory** | **Scaling** | **Database** | |---------------------|-----------------|---------------------|----------------------------------------------------------------------------------| -| Development | 1 CPU, 1 GB RAM | Up to 1 container | 10 GB disk, no backups | -| Production | 2 CPU, 2 GB RAM | Up to 10 containers | Autoscaling disk, automatic backups, highly available (multi-zone configuration) | +| Development | 1 CPU, 1 GB RAM | Up to 1 replica | 10 GB disk, no backups | +| Production | 2 CPU, 2 GB RAM | Up to 10 replicas | Autoscaling disk, automatic backups, highly available (multi-zone configuration) | -CPU and memory resources are per container. +CPU and memory resources are per replica. !!! warning "Immutable Deployment Type" Once a deployment is created, the deployment type cannot be changed. -!!! info "Resource Customization" - For `Production` type deployments, resources can be manually increased on a case-by-case basis depending on use case and capacity constraints. Contact support@langchain.dev to request an increase in resources. +!!! info "Self-Hosted Deployment" + Resources for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments can be fully customized. Deployment types are only applicable for [Cloud SaaS](../concepts/langgraph_cloud.md) deployments. - For `Development` types deployments, database disk size can be manually increased on a case-by-case basis depending on use case and capacity constraints. For most use cases, [TTLs](../how-tos/ttl/configure_ttl.md) should be configured to manage disk usage. Contact support@langchain.dev to request an increase in resources. +#### Production - Resources for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments can be fully customized. +`Production` type deployments are suitable for "production" workloads. For example, select `Production` for customer-facing applications in the critical path. + +Resources for `Production` type deployments can be manually increased on a case-by-case basis depending on use case and capacity constraints. Contact support@langchain.dev to request an increase in resources. + +#### Development + +`Development` type deployments are suitable development and testing. For example, select `Development` for internal testing environments. `Development` type deployments are not suitable for "production" workloads. + +!!! danger "Preemptible Compute Infrastructure" + `Development` type deployments (API server, queue server, and database) are provisioned on preemptible compute infrastructure. This means the compute infrastructure **may be terminated at any time without notice**. This may result in intermittent... + + - Redis connection timeouts/errors + - Postgres connection timeouts/errors + - Failed or retrying background runs + + This behavior is expected. Preemptible compute infrastructure **significantly reduces the cost to provision a `Development` type deployment**. By design, LangGraph Server is fault-tolerant. The implementation will automatically attempt to recover from Redis/Postgres connection errors and retry failed background runs. + + `Production` type deployments are provisioned on durable compute infrastructure, not preemptible compute infrastructure. + +Database disk size for `Development` type deployments can be manually increased on a case-by-case basis depending on use case and capacity constraints. For most use cases, [TTLs](../how-tos/ttl/configure_ttl.md) should be configured to manage disk usage. Contact support@langchain.dev to request an increase in resources. ### Database Provisioning From 690b6f4ea11ed699ca3de030cc14c92ee5651c03 Mon Sep 17 00:00:00 2001 From: Tat Dat Duong Date: Fri, 4 Jul 2025 16:29:38 +0200 Subject: [PATCH 02/31] fix(docs): invalid command for creating langgraph app Closes https://github.com/langchain-ai/langgraphjs/issues/1331 --- docs/docs/concepts/template_applications.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/concepts/template_applications.md b/docs/docs/concepts/template_applications.md index 76ebff698..8da3229b9 100644 --- a/docs/docs/concepts/template_applications.md +++ b/docs/docs/concepts/template_applications.md @@ -64,7 +64,7 @@ To create a new app from a template, use the `langgraph new` command. === "JS" ```bash - npx @langchain/langgraph-cli new + npm create langgraph@latest ``` ## Next Steps From cac0cd5522b7c1d7a3c38b6402b96f1e56b6e5de Mon Sep 17 00:00:00 2001 From: Chris G Date: Mon, 7 Jul 2025 02:35:37 +0200 Subject: [PATCH 03/31] docs: update name of CompiledGraph CompiledStateGraph (#5348) The name (and type) has changed. See: https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.state.StateGraph.compile --- docs/docs/tutorials/get-started/1-build-basic-chatbot.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/tutorials/get-started/1-build-basic-chatbot.md b/docs/docs/tutorials/get-started/1-build-basic-chatbot.md index 9235fa65f..3304822c6 100644 --- a/docs/docs/tutorials/get-started/1-build-basic-chatbot.md +++ b/docs/docs/tutorials/get-started/1-build-basic-chatbot.md @@ -112,7 +112,7 @@ This tells the graph to terminate after running the chatbot node. ## 6. Compile the graph Before running the graph, we'll need to compile it. We can do so by calling `compile()` -on the graph builder. This creates a `CompiledGraph` we can invoke on our state. +on the graph builder. This creates a `CompiledStateGraph` we can invoke on our state. ```python graph = graph_builder.compile() From d8756f257ede24658b51c95dec315b8218adba0a Mon Sep 17 00:00:00 2001 From: Shivang Agarwal Date: Mon, 7 Jul 2025 06:06:28 +0530 Subject: [PATCH 04/31] Updated TAVILY Key Setup (#5346) --- docs/docs/tutorials/get-started/2-add-tools.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/docs/docs/tutorials/get-started/2-add-tools.md b/docs/docs/tutorials/get-started/2-add-tools.md index 33accd9a8..4d30ae522 100644 --- a/docs/docs/tutorials/get-started/2-add-tools.md +++ b/docs/docs/tutorials/get-started/2-add-tools.md @@ -23,12 +23,16 @@ pip install -U langchain-tavily Configure your environment with your search engine API key: -```bash +```python +def _set_env(var: str): + if not os.environ.get(var): + os.environ[var] = getpass.getpass(f"{var}: ") + _set_env("TAVILY_API_KEY") ``` ``` -TAVILY_API_KEY: ········ +os.environ["TAVILY_API_KEY"]: "········" ``` ## 3. Define the tool From 269590c4d92a59daebd5f8646e9ab8cc8adff401 Mon Sep 17 00:00:00 2001 From: jessicaou Date: Sun, 6 Jul 2025 17:37:54 -0700 Subject: [PATCH 05/31] docs: update case studies (#5353) --- docs/docs/adopters.md | 29 +++++++++++++++++++++++------ 1 file changed, 23 insertions(+), 6 deletions(-) diff --git a/docs/docs/adopters.md b/docs/docs/adopters.md index 91142d44f..d2b8a278b 100644 --- a/docs/docs/adopters.md +++ b/docs/docs/adopters.md @@ -8,24 +8,41 @@ This list of companies using LangGraph and their success stories is compiled fro | [AirTop](https://www.airtop.ai/) | Software & Technology (GenAI Native) | Browser automation for AI agents | [Case study, 2024](https://blog.langchain.dev/customers-airtop/) | | [AppFolio](https://www.appfolio.com/) | Real Estate | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-appfolio/) | | [Athena Intelligence](https://www.athenaintel.com/) | Software & Technology (GenAI Native) | Research & summarization | [Case study, 2024](https://blog.langchain.dev/customers-athena-intelligence/) | +| [BlackRock](https://www.blackrock.com/) | Financial Services | Copilot for domain-specific task | [Interrupt talk, 2025](https://youtu.be/oyqeCHFM5U4?feature=shared) | | [Captide](https://www.captide.co/) | Software & Technology (GenAI Native) | Data extraction | [Case study, 2025](https://blog.langchain.dev/how-captide-is-redefining-equity-research-with-agentic-workflows-built-on-langgraph-and-langsmith/) | -| [Cisco Outshift](https://outshift.cisco.com/) | Software & Technology | DevOps | [Blog post, 2025](https://outshift.cisco.com/blog/build-react-agent-application-for-devops-tasks-using-rest-apis) | +| [Cisco CX](https://www.cisco.com/site/us/en/services/modern-data-center/index.html?CCID=cc005911&DTID=eivtotr001480&OID=srwsas032775) | Software & Technology | Customer support | [Interrupt Talk, 2025](https://youtu.be/gPhyPRtIMn0?feature=shared) | +| [Cisco Outshift](https://outshift.cisco.com/) | Software & Technology | DevOps | [Video story, 2025](https://www.youtube.com/watch?v=htcb-vGR_x0); [Case study, 2025](https://blog.langchain.com/cisco-outshift/); [Blog post, 2025](https://outshift.cisco.com/blog/build-react-agent-application-for-devops-tasks-using-rest-apis) | +| [Cisco TAC](https://www.cisco.com/c/en/us/support/index.html) | Software & Technology | Customer support | [Video story, 2025](https://youtu.be/EAj0HBDGqaE?feature=shared) | +| [City of Hope](https://www.cityofhope.org/) | Non-profit | Copilot for domain-specific task | [Video story, 2025](https://youtu.be/9ABwtK2gIZU?feature=shared) | | [C.H. Robinson](https://www.chrobinson.com/en-us/) | Logistics | Automation | [Case study, 2025](https://blog.langchain.dev/customers-chrobinson/) | +| [Definely](https://www.definely.com/) | Legal | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.com/customers-definely/) | +| [Docent Pro](https://docentpro.com/) | Travel | GenAI embedded product experiences | [Case study, 2025](https://blog.langchain.com/customers-docentpro/) | | [Elastic](https://www.elastic.co/) | Software & Technology | Copilot for domain-specific task | [Blog post, 2025](https://www.elastic.co/blog/elastic-security-generative-ai-features) | +| [Exa](https://exa.ai/) | Software & Technology (GenAI Native) | Search | [Case study, 2025](https://blog.langchain.com/exa/) | | [GitLab](https://about.gitlab.com/) | Software & Technology | Code generation | [Duo workflow docs](https://handbook.gitlab.com/handbook/engineering/architecture/design-documents/duo_workflow/) | +| [Harmonic](https://harmonic.ai/) | Software & Technology | Search | [Case study, 2025](https://blog.langchain.com/customers-harmonic/) | | [Inconvo](https://inconvo.ai/?ref=blog.langchain.dev) | Software & Technology | Code generation | [Case study, 2025](https://blog.langchain.dev/customers-inconvo/) | | [Infor](https://infor.com/) | Software & Technology | GenAI embedded product experiences; customer support; copilot | [Case study, 2025](https://blog.langchain.dev/customers-infor/) | +| [J.P. Morgan](https://www.jpmorganchase.com/) | Financial Services | Copilot for domain-specific task | [Interrupt talk, 2025](https://youtu.be/yMalr0jiOAc?feature=shared) | | [Klarna](https://www.klarna.com/) | Fintech | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.dev/customers-klarna/) | | [Komodo Health](https://www.komodohealth.com/) | Healthcare | Copilot for domain-specific task | [Blog post](https://www.komodohealth.com/perspectives/new-gen-ai-assistant-empowers-the-enterprise/) | -| [LinkedIn](https://www.linkedin.com/) | Social Media | Code generation; Search & discovery | [Blog post, 2025](https://www.linkedin.com/blog/engineering/ai/practical-text-to-sql-for-data-analytics); [Blog post, 2024](https://www.linkedin.com/blog/engineering/generative-ai/behind-the-platform-the-journey-to-create-the-linkedin-genai-application-tech-stack) | +| [LinkedIn](https://www.linkedin.com/) | Social Media | Code generation; Search & discovery | [Interrupt talk, 2025](https://youtu.be/NmblVxyBhi8?feature=shared); [Blog post, 2025](https://www.linkedin.com/blog/engineering/ai/practical-text-to-sql-for-data-analytics); [Blog post, 2024](https://www.linkedin.com/blog/engineering/generative-ai/behind-the-platform-the-journey-to-create-the-linkedin-genai-application-tech-stack) | | [Minimal](https://gominimal.ai/) | E-commerce | Customer support | [Case study, 2025](https://blog.langchain.dev/how-minimal-built-a-multi-agent-customer-support-system-with-langgraph-langsmith/) | +| [Modern Treasury](https://www.moderntreasury.com/) | Fintech | GenAI embedded product experiences | [Video story, 2025](https://youtu.be/AwAiffXqaCU?feature=shared) | +| [Monday](https://monday.com/) | Software & Technology | GenAI embedded product experiences | [Interrupt talk, 2025](https://blog.langchain.dev/how-minimal-built-a-multi-agent-customer-support-system-with-langgraph-langsmith/) | +| [Morningstar](https://www.morningstar.com/) | Financial Services | Research & summarization | [Video story, 2025](https://youtu.be/6LidoFXCJPs?feature=shared) | | [OpenRecovery](https://www.openrecovery.com/) | Healthcare | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-openrecovery/) | +| [Pigment](https://www.pigment.com/) | Fintech | GenAI embedded product experiences | [Video story, 2025](https://youtu.be/5JVSO2KYOmE?feature=shared) | +| [Prosper](https://www.prosper.com/) | Fintech | Customer support | [Video story, 2025](https://youtu.be/9RFNOYtkwsc?feature=shared) | | [Qodo](https://www.qodo.ai/) | Software & Technology (GenAI Native) | Code generation | [Blog post, 2025](https://www.qodo.ai/blog/why-we-chose-langgraph-to-build-our-coding-agent/) | -| [Rakuten](https://www.rakuten.com/) | E-commerce / Fintech | Copilot for domain-specific task | [Blog post, 2025](https://rakuten.today/blog/from-ai-hype-to-real-world-tools-rakuten-teams-up-with-langchain.html) | +| [Rakuten](https://www.rakuten.com/) | E-commerce / Fintech | Copilot for domain-specific task | [Video story, 2025](https://youtu.be/gD1LIjCkuA8?feature=shared); [Blog post, 2025](https://rakuten.today/blog/from-ai-hype-to-real-world-tools-rakuten-teams-up-with-langchain.html) | | [Replit](https://replit.com/) | Software & Technology | Code generation | [Blog post, 2024](https://blog.langchain.dev/customers-replit/); [Breakout agent story, 2024](https://www.langchain.com/breakoutagents/replit); [Fireside chat video, 2024](https://www.youtube.com/watch?v=ViykMqljjxU) | | [Rexera](https://www.rexera.com/) | Real Estate (GenAI Native) | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-rexera/) | +| [Abu Dhabi Government](https://www.tamm.abudhabi/) | Government | Search | [Case study, 2025](https://blog.langchain.com/customers-abu-dhabi-government/) | | [Tradestack](https://www.tradestack.uk/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-tradestack/) | -| [Uber](https://www.uber.com/) | Transportation | Developer productivity; Code generation | [Presentation, 2024](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/); [Video, 2024](https://www.youtube.com/watch?v=8rkA5vWUE4Y) | -| [Unify](https://www.unifygtm.com/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Blog post, 2024](https://blog.langchain.dev/unify-launches-agents-for-account-qualification-using-langgraph-and-langsmith/) | -| [Vizient](https://www.vizientinc.com/) | Healthcare | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.dev/p/3d2cd58c-13a5-4df9-bd84-7d54ed0ed82c/) | +| [Uber](https://www.uber.com/) | Transportation | Developer productivity; Code generation | [Interrupt talk, 2025](https://youtu.be/Bugs0dVcNI8?feature=shared); [Presentation, 2024](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/); [Video, 2024](https://www.youtube.com/watch?v=8rkA5vWUE4Y) | +| [Unify](https://www.unifygtm.com/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Interrupt talk, 2025](https://youtu.be/pKk-LfhujwI?feature=shared); [Blog post, 2024](https://blog.langchain.dev/unify-launches-agents-for-account-qualification-using-langgraph-and-langsmith/) | +| [Vizient](https://www.vizientinc.com/) | Healthcare | Copilot for domain-specific task | [Video story, 2025](https://www.youtube.com/watch?v=vrjJ6NuyTWA); [Case study, 2025](https://blog.langchain.dev/p/3d2cd58c-13a5-4df9-bd84-7d54ed0ed82c/) | | [Vodafone](https://www.vodafone.com/) | Telecommunications | Code generation; internal search | [Case study, 2025](https://blog.langchain.dev/customers-vodafone/) | +| [WebToon](https://www.webtoons.com/en/) | Media & Entertainment | Data extraction | [Case study, 2025](https://blog.langchain.com/customers-webtoon/) | +| [11x](https://www.11x.ai/) | Software & Technology (GenAI Native) | Research & outreach | [Interrupt talk, 2025](https://youtu.be/fegwPmaAPQk?feature=shared) | From 4abf94846241f3546a0194efd40e109745e7d005 Mon Sep 17 00:00:00 2001 From: lc-arjun Date: Mon, 7 Jul 2025 08:05:46 -0700 Subject: [PATCH 06/31] docs: add -tunnel flag for node server (#5373) add -tunnel flag for node server --- docs/docs/troubleshooting/studio.md | 4 +- libs/sdk-js/client.cjs | 1 + libs/sdk-js/client.d.cts | 1 + libs/sdk-js/client.d.ts | 1 + libs/sdk-js/client.js | 1 + libs/sdk-js/docs/README.md | 26 + libs/sdk-js/docs/auth/README.md | 18 + libs/sdk-js/docs/auth/classes/Auth.md | 75 +++ .../sdk-js/docs/auth/classes/HTTPException.md | 185 +++++++ .../docs/auth/interfaces/AuthEventValueMap.md | 481 ++++++++++++++++++ .../docs/auth/type-aliases/AuthFilters.md | 15 + libs/sdk-js/docs/classes/AssistantsClient.md | 385 ++++++++++++++ libs/sdk-js/docs/classes/Client.md | 98 ++++ libs/sdk-js/docs/classes/CronsClient.md | 151 ++++++ libs/sdk-js/docs/classes/RunsClient.md | 469 +++++++++++++++++ libs/sdk-js/docs/classes/StoreClient.md | 314 ++++++++++++ libs/sdk-js/docs/classes/ThreadsClient.md | 433 ++++++++++++++++ libs/sdk-js/docs/functions/getApiKey.md | 32 ++ libs/sdk-js/docs/interfaces/ClientConfig.md | 57 +++ libs/sdk-js/docs/react/README.md | 18 + libs/sdk-js/docs/react/functions/useStream.md | 27 + .../sdk-js/docs/react/interfaces/UseStream.md | 227 +++++++++ .../docs/react/interfaces/UseStreamOptions.md | 343 +++++++++++++ .../react/type-aliases/MessageMetadata.md | 42 ++ libs/sdk-js/docs/type-aliases/RequestHook.md | 25 + libs/sdk-js/index.cjs | 1 + libs/sdk-js/index.d.cts | 1 + libs/sdk-js/index.d.ts | 1 + libs/sdk-js/index.js | 1 + libs/sdk-js/react.cjs | 1 + libs/sdk-js/react.d.cts | 1 + libs/sdk-js/react.d.ts | 1 + libs/sdk-js/react.js | 1 + 33 files changed, 3435 insertions(+), 2 deletions(-) create mode 100644 libs/sdk-js/client.cjs create mode 100644 libs/sdk-js/client.d.cts create mode 100644 libs/sdk-js/client.d.ts create mode 100644 libs/sdk-js/client.js create mode 100644 libs/sdk-js/docs/README.md create mode 100644 libs/sdk-js/docs/auth/README.md create mode 100644 libs/sdk-js/docs/auth/classes/Auth.md create mode 100644 libs/sdk-js/docs/auth/classes/HTTPException.md create mode 100644 libs/sdk-js/docs/auth/interfaces/AuthEventValueMap.md create mode 100644 libs/sdk-js/docs/auth/type-aliases/AuthFilters.md create mode 100644 libs/sdk-js/docs/classes/AssistantsClient.md create mode 100644 libs/sdk-js/docs/classes/Client.md create mode 100644 libs/sdk-js/docs/classes/CronsClient.md create mode 100644 libs/sdk-js/docs/classes/RunsClient.md create mode 100644 libs/sdk-js/docs/classes/StoreClient.md create mode 100644 libs/sdk-js/docs/classes/ThreadsClient.md create mode 100644 libs/sdk-js/docs/functions/getApiKey.md create mode 100644 libs/sdk-js/docs/interfaces/ClientConfig.md create mode 100644 libs/sdk-js/docs/react/README.md create mode 100644 libs/sdk-js/docs/react/functions/useStream.md create mode 100644 libs/sdk-js/docs/react/interfaces/UseStream.md create mode 100644 libs/sdk-js/docs/react/interfaces/UseStreamOptions.md create mode 100644 libs/sdk-js/docs/react/type-aliases/MessageMetadata.md create mode 100644 libs/sdk-js/docs/type-aliases/RequestHook.md create mode 100644 libs/sdk-js/index.cjs create mode 100644 libs/sdk-js/index.d.cts create mode 100644 libs/sdk-js/index.d.ts create mode 100644 libs/sdk-js/index.js create mode 100644 libs/sdk-js/react.cjs create mode 100644 libs/sdk-js/react.d.cts create mode 100644 libs/sdk-js/react.d.ts create mode 100644 libs/sdk-js/react.js diff --git a/docs/docs/troubleshooting/studio.md b/docs/docs/troubleshooting/studio.md index b06f325de..3fcbdea7f 100644 --- a/docs/docs/troubleshooting/studio.md +++ b/docs/docs/troubleshooting/studio.md @@ -17,7 +17,7 @@ Safari blocks plain-HTTP traffic on localhost. When running Studio with `langgra ```shell # Requires @langchain/langgraph-cli>=0.0.26 - npx @langchain/langgraph-cli dev + npx @langchain/langgraph-cli dev --tunnel ``` The command outputs a URL in this format: @@ -55,7 +55,7 @@ Disable Brave Shields for LangSmith using the Brave icon in the URL bar. ```shell # Requires @langchain/langgraph-cli>=0.0.26 - npx @langchain/langgraph-cli dev + npx @langchain/langgraph-cli dev --tunnel ``` The command outputs a URL in this format: diff --git a/libs/sdk-js/client.cjs b/libs/sdk-js/client.cjs new file mode 100644 index 000000000..680e5bd38 --- /dev/null +++ b/libs/sdk-js/client.cjs @@ -0,0 +1 @@ +module.exports = require('./dist/client.cjs'); \ No newline at end of file diff --git a/libs/sdk-js/client.d.cts b/libs/sdk-js/client.d.cts new file mode 100644 index 000000000..1c03ff9cb --- /dev/null +++ b/libs/sdk-js/client.d.cts @@ -0,0 +1 @@ +export * from './dist/client.js' \ No newline at end of file diff --git a/libs/sdk-js/client.d.ts b/libs/sdk-js/client.d.ts new file mode 100644 index 000000000..1c03ff9cb --- /dev/null +++ b/libs/sdk-js/client.d.ts @@ -0,0 +1 @@ +export * from './dist/client.js' \ No newline at end of file diff --git a/libs/sdk-js/client.js b/libs/sdk-js/client.js new file mode 100644 index 000000000..1c03ff9cb --- /dev/null +++ b/libs/sdk-js/client.js @@ -0,0 +1 @@ +export * from './dist/client.js' \ No newline at end of file diff --git a/libs/sdk-js/docs/README.md b/libs/sdk-js/docs/README.md new file mode 100644 index 000000000..a1627b448 --- /dev/null +++ b/libs/sdk-js/docs/README.md @@ -0,0 +1,26 @@ +**@langchain/langgraph-sdk** + +*** + +# @langchain/langgraph-sdk + +## Classes + +- [AssistantsClient](classes/AssistantsClient.md) +- [Client](classes/Client.md) +- [CronsClient](classes/CronsClient.md) +- [RunsClient](classes/RunsClient.md) +- [StoreClient](classes/StoreClient.md) +- [ThreadsClient](classes/ThreadsClient.md) + +## Interfaces + +- [ClientConfig](interfaces/ClientConfig.md) + +## Type Aliases + +- [RequestHook](type-aliases/RequestHook.md) + +## Functions + +- [getApiKey](functions/getApiKey.md) diff --git a/libs/sdk-js/docs/auth/README.md b/libs/sdk-js/docs/auth/README.md new file mode 100644 index 000000000..351cb8490 --- /dev/null +++ b/libs/sdk-js/docs/auth/README.md @@ -0,0 +1,18 @@ +**@langchain/langgraph-sdk** + +*** + +# @langchain/langgraph-sdk/auth + +## Classes + +- [Auth](classes/Auth.md) +- [HTTPException](classes/HTTPException.md) + +## Interfaces + +- [AuthEventValueMap](interfaces/AuthEventValueMap.md) + +## Type Aliases + +- [AuthFilters](type-aliases/AuthFilters.md) diff --git a/libs/sdk-js/docs/auth/classes/Auth.md b/libs/sdk-js/docs/auth/classes/Auth.md new file mode 100644 index 000000000..4b4cc618a --- /dev/null +++ b/libs/sdk-js/docs/auth/classes/Auth.md @@ -0,0 +1,75 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / Auth + +# Class: Auth\ + +Defined in: [src/auth/index.ts:11](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/index.ts#L11) + +## Type Parameters + +• **TExtra** = \{\} + +• **TAuthReturn** *extends* `BaseAuthReturn` = `BaseAuthReturn` + +• **TUser** *extends* `BaseUser` = `ToUserLike`\<`TAuthReturn`\> + +## Constructors + +### new Auth() + +> **new Auth**\<`TExtra`, `TAuthReturn`, `TUser`\>(): [`Auth`](Auth.md)\<`TExtra`, `TAuthReturn`, `TUser`\> + +#### Returns + +[`Auth`](Auth.md)\<`TExtra`, `TAuthReturn`, `TUser`\> + +## Methods + +### authenticate() + +> **authenticate**\<`T`\>(`cb`): [`Auth`](Auth.md)\<`TExtra`, `T`\> + +Defined in: [src/auth/index.ts:25](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/index.ts#L25) + +#### Type Parameters + +• **T** *extends* `BaseAuthReturn` + +#### Parameters + +##### cb + +`AuthenticateCallback`\<`T`\> + +#### Returns + +[`Auth`](Auth.md)\<`TExtra`, `T`\> + +*** + +### on() + +> **on**\<`T`\>(`event`, `callback`): `this` + +Defined in: [src/auth/index.ts:32](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/index.ts#L32) + +#### Type Parameters + +• **T** *extends* `CallbackEvent` + +#### Parameters + +##### event + +`T` + +##### callback + +`OnCallback`\<`T`, `TUser`\> + +#### Returns + +`this` diff --git a/libs/sdk-js/docs/auth/classes/HTTPException.md b/libs/sdk-js/docs/auth/classes/HTTPException.md new file mode 100644 index 000000000..0db191af5 --- /dev/null +++ b/libs/sdk-js/docs/auth/classes/HTTPException.md @@ -0,0 +1,185 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / HTTPException + +# Class: HTTPException + +Defined in: [src/auth/error.ts:66](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/error.ts#L66) + +## Extends + +- `Error` + +## Constructors + +### new HTTPException() + +> **new HTTPException**(`status`, `options`?): [`HTTPException`](HTTPException.md) + +Defined in: [src/auth/error.ts:70](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/error.ts#L70) + +#### Parameters + +##### status + +`number` + +##### options? + +###### cause? + +`unknown` + +###### headers? + +`HeadersInit` + +###### message? + +`string` + +#### Returns + +[`HTTPException`](HTTPException.md) + +#### Overrides + +`Error.constructor` + +## Properties + +### cause? + +> `optional` **cause**: `unknown` + +Defined in: node\_modules/typescript/lib/lib.es2022.error.d.ts:24 + +#### Inherited from + +`Error.cause` + +*** + +### headers + +> **headers**: `HeadersInit` + +Defined in: [src/auth/error.ts:68](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/error.ts#L68) + +*** + +### message + +> **message**: `string` + +Defined in: node\_modules/typescript/lib/lib.es5.d.ts:1077 + +#### Inherited from + +`Error.message` + +*** + +### name + +> **name**: `string` + +Defined in: node\_modules/typescript/lib/lib.es5.d.ts:1076 + +#### Inherited from + +`Error.name` + +*** + +### stack? + +> `optional` **stack**: `string` + +Defined in: node\_modules/typescript/lib/lib.es5.d.ts:1078 + +#### Inherited from + +`Error.stack` + +*** + +### status + +> **status**: `number` + +Defined in: [src/auth/error.ts:67](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/error.ts#L67) + +*** + +### prepareStackTrace()? + +> `static` `optional` **prepareStackTrace**: (`err`, `stackTraces`) => `any` + +Defined in: node\_modules/@types/node/globals.d.ts:28 + +Optional override for formatting stack traces + +#### Parameters + +##### err + +`Error` + +##### stackTraces + +`CallSite`[] + +#### Returns + +`any` + +#### See + +https://v8.dev/docs/stack-trace-api#customizing-stack-traces + +#### Inherited from + +`Error.prepareStackTrace` + +*** + +### stackTraceLimit + +> `static` **stackTraceLimit**: `number` + +Defined in: node\_modules/@types/node/globals.d.ts:30 + +#### Inherited from + +`Error.stackTraceLimit` + +## Methods + +### captureStackTrace() + +> `static` **captureStackTrace**(`targetObject`, `constructorOpt`?): `void` + +Defined in: node\_modules/@types/node/globals.d.ts:21 + +Create .stack property on a target object + +#### Parameters + +##### targetObject + +`object` + +##### constructorOpt? + +`Function` + +#### Returns + +`void` + +#### Inherited from + +`Error.captureStackTrace` diff --git a/libs/sdk-js/docs/auth/interfaces/AuthEventValueMap.md b/libs/sdk-js/docs/auth/interfaces/AuthEventValueMap.md new file mode 100644 index 000000000..686e1a39b --- /dev/null +++ b/libs/sdk-js/docs/auth/interfaces/AuthEventValueMap.md @@ -0,0 +1,481 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / AuthEventValueMap + +# Interface: AuthEventValueMap + +Defined in: [src/auth/types.ts:218](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L218) + +## Properties + +### assistants:create + +> **assistants:create**: `object` + +Defined in: [src/auth/types.ts:226](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L226) + +#### assistant\_id? + +> `optional` **assistant\_id**: `Maybe`\<`string`\> + +#### config? + +> `optional` **config**: `Maybe`\<`AssistantConfig`\> + +#### graph\_id + +> **graph\_id**: `string` + +#### if\_exists? + +> `optional` **if\_exists**: `Maybe`\<`"raise"` \| `"do_nothing"`\> + +#### metadata? + +> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> + +#### name? + +> `optional` **name**: `Maybe`\<`string`\> + +*** + +### assistants:delete + +> **assistants:delete**: `object` + +Defined in: [src/auth/types.ts:229](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L229) + +#### assistant\_id + +> **assistant\_id**: `string` + +*** + +### assistants:read + +> **assistants:read**: `object` + +Defined in: [src/auth/types.ts:227](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L227) + +#### assistant\_id + +> **assistant\_id**: `string` + +#### metadata? + +> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> + +*** + +### assistants:search + +> **assistants:search**: `object` + +Defined in: [src/auth/types.ts:230](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L230) + +#### graph\_id? + +> `optional` **graph\_id**: `Maybe`\<`string`\> + +#### limit? + +> `optional` **limit**: `Maybe`\<`number`\> + +#### metadata? + +> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> + +#### offset? + +> `optional` **offset**: `Maybe`\<`number`\> + +*** + +### assistants:update + +> **assistants:update**: `object` + +Defined in: [src/auth/types.ts:228](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L228) + +#### assistant\_id + +> **assistant\_id**: `string` + +#### config? + +> `optional` **config**: `Maybe`\<`AssistantConfig`\> + +#### graph\_id? + +> `optional` **graph\_id**: `Maybe`\<`string`\> + +#### metadata? + +> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> + +#### name? + +> `optional` **name**: `Maybe`\<`string`\> + +#### version? + +> `optional` **version**: `Maybe`\<`number`\> + +*** + +### crons:create + +> **crons:create**: `object` + +Defined in: [src/auth/types.ts:232](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L232) + +#### cron\_id? + +> `optional` **cron\_id**: `Maybe`\<`string`\> + +#### end\_time? + +> `optional` **end\_time**: `Maybe`\<`string`\> + +#### payload? + +> `optional` **payload**: `Maybe`\<`Record`\<`string`, `unknown`\>\> + +#### schedule + +> **schedule**: `string` + +#### thread\_id? + +> `optional` **thread\_id**: `Maybe`\<`string`\> + +#### user\_id? + +> `optional` **user\_id**: `Maybe`\<`string`\> + +*** + +### crons:delete + +> **crons:delete**: `object` + +Defined in: [src/auth/types.ts:235](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L235) + +#### cron\_id + +> **cron\_id**: `string` + +*** + +### crons:read + +> **crons:read**: `object` + +Defined in: [src/auth/types.ts:233](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L233) + +#### cron\_id + +> **cron\_id**: `string` + +*** + +### crons:search + +> **crons:search**: `object` + +Defined in: [src/auth/types.ts:236](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L236) + +#### assistant\_id? + +> `optional` **assistant\_id**: `Maybe`\<`string`\> + +#### limit? + +> `optional` **limit**: `Maybe`\<`number`\> + +#### offset? + +> `optional` **offset**: `Maybe`\<`number`\> + +#### thread\_id? + +> `optional` **thread\_id**: `Maybe`\<`string`\> + +*** + +### crons:update + +> **crons:update**: `object` + +Defined in: [src/auth/types.ts:234](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L234) + +#### cron\_id + +> **cron\_id**: `string` + +#### payload? + +> `optional` **payload**: `Maybe`\<`Record`\<`string`, `unknown`\>\> + +#### schedule? + +> `optional` **schedule**: `Maybe`\<`string`\> + +*** + +### store:delete + +> **store:delete**: `object` + +Defined in: [src/auth/types.ts:242](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L242) + +#### key + +> **key**: `string` + +#### namespace? + +> `optional` **namespace**: `Maybe`\<`string`[]\> + +*** + +### store:get + +> **store:get**: `object` + +Defined in: [src/auth/types.ts:239](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L239) + +#### key + +> **key**: `string` + +#### namespace + +> **namespace**: `Maybe`\<`string`[]\> + +*** + +### store:list\_namespaces + +> **store:list\_namespaces**: `object` + +Defined in: [src/auth/types.ts:241](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L241) + +#### limit? + +> `optional` **limit**: `Maybe`\<`number`\> + +#### max\_depth? + +> `optional` **max\_depth**: `Maybe`\<`number`\> + +#### namespace? + +> `optional` **namespace**: `Maybe`\<`string`[]\> + +#### offset? + +> `optional` **offset**: `Maybe`\<`number`\> + +#### suffix? + +> `optional` **suffix**: `Maybe`\<`string`[]\> + +*** + +### store:put + +> **store:put**: `object` + +Defined in: [src/auth/types.ts:238](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L238) + +#### key + +> **key**: `string` + +#### namespace + +> **namespace**: `string`[] + +#### value + +> **value**: `Record`\<`string`, `unknown`\> + +*** + +### store:search + +> **store:search**: `object` + +Defined in: [src/auth/types.ts:240](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L240) + +#### filter? + +> `optional` **filter**: `Maybe`\<`Record`\<`string`, `unknown`\>\> + +#### limit? + +> `optional` **limit**: `Maybe`\<`number`\> + +#### namespace? + +> `optional` **namespace**: `Maybe`\<`string`[]\> + +#### offset? + +> `optional` **offset**: `Maybe`\<`number`\> + +#### query? + +> `optional` **query**: `Maybe`\<`string`\> + +*** + +### threads:create + +> **threads:create**: `object` + +Defined in: [src/auth/types.ts:219](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L219) + +#### if\_exists? + +> `optional` **if\_exists**: `Maybe`\<`"raise"` \| `"do_nothing"`\> + +#### metadata? + +> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> + +#### thread\_id? + +> `optional` **thread\_id**: `Maybe`\<`string`\> + +*** + +### threads:create\_run + +> **threads:create\_run**: `object` + +Defined in: [src/auth/types.ts:224](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L224) + +#### after\_seconds? + +> `optional` **after\_seconds**: `Maybe`\<`number`\> + +#### assistant\_id + +> **assistant\_id**: `string` + +#### if\_not\_exists? + +> `optional` **if\_not\_exists**: `Maybe`\<`"reject"` \| `"create"`\> + +#### kwargs + +> **kwargs**: `Record`\<`string`, `unknown`\> + +#### metadata? + +> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> + +#### multitask\_strategy? + +> `optional` **multitask\_strategy**: `Maybe`\<`"reject"` \| `"interrupt"` \| `"rollback"` \| `"enqueue"`\> + +#### prevent\_insert\_if\_inflight? + +> `optional` **prevent\_insert\_if\_inflight**: `Maybe`\<`boolean`\> + +#### run\_id + +> **run\_id**: `string` + +#### status + +> **status**: `Maybe`\<`"pending"` \| `"running"` \| `"error"` \| `"success"` \| `"timeout"` \| `"interrupted"`\> + +#### thread\_id? + +> `optional` **thread\_id**: `Maybe`\<`string`\> + +*** + +### threads:delete + +> **threads:delete**: `object` + +Defined in: [src/auth/types.ts:222](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L222) + +#### run\_id? + +> `optional` **run\_id**: `Maybe`\<`string`\> + +#### thread\_id? + +> `optional` **thread\_id**: `Maybe`\<`string`\> + +*** + +### threads:read + +> **threads:read**: `object` + +Defined in: [src/auth/types.ts:220](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L220) + +#### thread\_id? + +> `optional` **thread\_id**: `Maybe`\<`string`\> + +*** + +### threads:search + +> **threads:search**: `object` + +Defined in: [src/auth/types.ts:223](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L223) + +#### limit? + +> `optional` **limit**: `Maybe`\<`number`\> + +#### metadata? + +> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> + +#### offset? + +> `optional` **offset**: `Maybe`\<`number`\> + +#### status? + +> `optional` **status**: `Maybe`\<`"error"` \| `"interrupted"` \| `"idle"` \| `"busy"` \| `string` & `object`\> + +#### thread\_id? + +> `optional` **thread\_id**: `Maybe`\<`string`\> + +#### values? + +> `optional` **values**: `Maybe`\<`Record`\<`string`, `unknown`\>\> + +*** + +### threads:update + +> **threads:update**: `object` + +Defined in: [src/auth/types.ts:221](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L221) + +#### action? + +> `optional` **action**: `Maybe`\<`"interrupt"` \| `"rollback"`\> + +#### metadata? + +> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> + +#### thread\_id? + +> `optional` **thread\_id**: `Maybe`\<`string`\> diff --git a/libs/sdk-js/docs/auth/type-aliases/AuthFilters.md b/libs/sdk-js/docs/auth/type-aliases/AuthFilters.md new file mode 100644 index 000000000..b9b2e1fb2 --- /dev/null +++ b/libs/sdk-js/docs/auth/type-aliases/AuthFilters.md @@ -0,0 +1,15 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / AuthFilters + +# Type Alias: AuthFilters\ + +> **AuthFilters**\<`TKey`\>: \{ \[key in TKey\]: string \| \{ \[op in "$contains" \| "$eq"\]?: string \} \} + +Defined in: [src/auth/types.ts:367](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L367) + +## Type Parameters + +• **TKey** *extends* `string` \| `number` \| `symbol` diff --git a/libs/sdk-js/docs/classes/AssistantsClient.md b/libs/sdk-js/docs/classes/AssistantsClient.md new file mode 100644 index 000000000..571dc16e8 --- /dev/null +++ b/libs/sdk-js/docs/classes/AssistantsClient.md @@ -0,0 +1,385 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / AssistantsClient + +# Class: AssistantsClient + +Defined in: [client.ts:431](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L431) + +## Extends + +- `BaseClient` + +## Constructors + +### new AssistantsClient() + +> **new AssistantsClient**(`config`?): [`AssistantsClient`](AssistantsClient.md) + +Defined in: [client.ts:183](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L183) + +#### Parameters + +##### config? + +[`ClientConfig`](../interfaces/ClientConfig.md) + +#### Returns + +[`AssistantsClient`](AssistantsClient.md) + +#### Inherited from + +`BaseClient.constructor` + +## Methods + +### create() + +> **create**(`payload`): `Promise`\<`Assistant`\> + +Defined in: [client.ts:496](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L496) + +Create a new assistant. + +#### Parameters + +##### payload + +Payload for creating an assistant. + +###### assistantId? + +`string` + +###### config? + +`Config` + +###### description? + +`string` + +###### graphId + +`string` + +###### ifExists? + +`OnConflictBehavior` + +###### metadata? + +`Metadata` + +###### name? + +`string` + +#### Returns + +`Promise`\<`Assistant`\> + +The created assistant. + +*** + +### delete() + +> **delete**(`assistantId`): `Promise`\<`void`\> + +Defined in: [client.ts:552](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L552) + +Delete an assistant. + +#### Parameters + +##### assistantId + +`string` + +ID of the assistant. + +#### Returns + +`Promise`\<`void`\> + +*** + +### get() + +> **get**(`assistantId`): `Promise`\<`Assistant`\> + +Defined in: [client.ts:438](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L438) + +Get an assistant by ID. + +#### Parameters + +##### assistantId + +`string` + +The ID of the assistant. + +#### Returns + +`Promise`\<`Assistant`\> + +Assistant + +*** + +### getGraph() + +> **getGraph**(`assistantId`, `options`?): `Promise`\<`AssistantGraph`\> + +Defined in: [client.ts:448](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L448) + +Get the JSON representation of the graph assigned to a runnable + +#### Parameters + +##### assistantId + +`string` + +The ID of the assistant. + +##### options? + +###### xray? + +`number` \| `boolean` + +Whether to include subgraphs in the serialized graph representation. If an integer value is provided, only subgraphs with a depth less than or equal to the value will be included. + +#### Returns + +`Promise`\<`AssistantGraph`\> + +Serialized graph + +*** + +### getSchemas() + +> **getSchemas**(`assistantId`): `Promise`\<`GraphSchema`\> + +Defined in: [client.ts:462](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L462) + +Get the state and config schema of the graph assigned to a runnable + +#### Parameters + +##### assistantId + +`string` + +The ID of the assistant. + +#### Returns + +`Promise`\<`GraphSchema`\> + +Graph schema + +*** + +### getSubgraphs() + +> **getSubgraphs**(`assistantId`, `options`?): `Promise`\<`Subgraphs`\> + +Defined in: [client.ts:473](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L473) + +Get the schemas of an assistant by ID. + +#### Parameters + +##### assistantId + +`string` + +The ID of the assistant to get the schema of. + +##### options? + +Additional options for getting subgraphs, such as namespace or recursion extraction. + +###### namespace? + +`string` + +###### recurse? + +`boolean` + +#### Returns + +`Promise`\<`Subgraphs`\> + +The subgraphs of the assistant. + +*** + +### getVersions() + +> **getVersions**(`assistantId`, `payload`?): `Promise`\<`AssistantVersion`[]\> + +Defined in: [client.ts:590](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L590) + +List all versions of an assistant. + +#### Parameters + +##### assistantId + +`string` + +ID of the assistant. + +##### payload? + +###### limit? + +`number` + +###### metadata? + +`Metadata` + +###### offset? + +`number` + +#### Returns + +`Promise`\<`AssistantVersion`[]\> + +List of assistant versions. + +*** + +### search() + +> **search**(`query`?): `Promise`\<`Assistant`[]\> + +Defined in: [client.ts:563](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L563) + +List assistants. + +#### Parameters + +##### query? + +Query options. + +###### graphId? + +`string` + +###### limit? + +`number` + +###### metadata? + +`Metadata` + +###### offset? + +`number` + +###### sortBy? + +`AssistantSortBy` + +###### sortOrder? + +`SortOrder` + +#### Returns + +`Promise`\<`Assistant`[]\> + +List of assistants. + +*** + +### setLatest() + +> **setLatest**(`assistantId`, `version`): `Promise`\<`Assistant`\> + +Defined in: [client.ts:618](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L618) + +Change the version of an assistant. + +#### Parameters + +##### assistantId + +`string` + +ID of the assistant. + +##### version + +`number` + +The version to change to. + +#### Returns + +`Promise`\<`Assistant`\> + +The updated assistant. + +*** + +### update() + +> **update**(`assistantId`, `payload`): `Promise`\<`Assistant`\> + +Defined in: [client.ts:525](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L525) + +Update an assistant. + +#### Parameters + +##### assistantId + +`string` + +ID of the assistant. + +##### payload + +Payload for updating the assistant. + +###### config? + +`Config` + +###### description? + +`string` + +###### graphId? + +`string` + +###### metadata? + +`Metadata` + +###### name? + +`string` + +#### Returns + +`Promise`\<`Assistant`\> + +The updated assistant. diff --git a/libs/sdk-js/docs/classes/Client.md b/libs/sdk-js/docs/classes/Client.md new file mode 100644 index 000000000..b0afc23de --- /dev/null +++ b/libs/sdk-js/docs/classes/Client.md @@ -0,0 +1,98 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / Client + +# Class: Client\ + +Defined in: [client.ts:1620](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1620) + +## Type Parameters + +• **TStateType** = `DefaultValues` + +• **TUpdateType** = `TStateType` + +• **TCustomEventType** = `unknown` + +## Constructors + +### new Client() + +> **new Client**\<`TStateType`, `TUpdateType`, `TCustomEventType`\>(`config`?): [`Client`](Client.md)\<`TStateType`, `TUpdateType`, `TCustomEventType`\> + +Defined in: [client.ts:1656](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1656) + +#### Parameters + +##### config? + +[`ClientConfig`](../interfaces/ClientConfig.md) + +#### Returns + +[`Client`](Client.md)\<`TStateType`, `TUpdateType`, `TCustomEventType`\> + +## Properties + +### ~ui + +> **~ui**: `UiClient` + +Defined in: [client.ts:1654](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1654) + +**`Internal`** + +The client for interacting with the UI. + Used by LoadExternalComponent and the API might change in the future. + +*** + +### assistants + +> **assistants**: [`AssistantsClient`](AssistantsClient.md) + +Defined in: [client.ts:1628](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1628) + +The client for interacting with assistants. + +*** + +### crons + +> **crons**: [`CronsClient`](CronsClient.md) + +Defined in: [client.ts:1643](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1643) + +The client for interacting with cron runs. + +*** + +### runs + +> **runs**: [`RunsClient`](RunsClient.md)\<`TStateType`, `TUpdateType`, `TCustomEventType`\> + +Defined in: [client.ts:1638](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1638) + +The client for interacting with runs. + +*** + +### store + +> **store**: [`StoreClient`](StoreClient.md) + +Defined in: [client.ts:1648](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1648) + +The client for interacting with the KV store. + +*** + +### threads + +> **threads**: [`ThreadsClient`](ThreadsClient.md)\<`TStateType`, `TUpdateType`\> + +Defined in: [client.ts:1633](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1633) + +The client for interacting with threads. diff --git a/libs/sdk-js/docs/classes/CronsClient.md b/libs/sdk-js/docs/classes/CronsClient.md new file mode 100644 index 000000000..ae0017d9c --- /dev/null +++ b/libs/sdk-js/docs/classes/CronsClient.md @@ -0,0 +1,151 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / CronsClient + +# Class: CronsClient + +Defined in: [client.ts:334](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L334) + +## Extends + +- `BaseClient` + +## Constructors + +### new CronsClient() + +> **new CronsClient**(`config`?): [`CronsClient`](CronsClient.md) + +Defined in: [client.ts:183](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L183) + +#### Parameters + +##### config? + +[`ClientConfig`](../interfaces/ClientConfig.md) + +#### Returns + +[`CronsClient`](CronsClient.md) + +#### Inherited from + +`BaseClient.constructor` + +## Methods + +### create() + +> **create**(`assistantId`, `payload`?): `Promise`\<`CronCreateResponse`\> + +Defined in: [client.ts:375](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L375) + +#### Parameters + +##### assistantId + +`string` + +Assistant ID to use for this cron job. + +##### payload? + +`CronsCreatePayload` + +Payload for creating a cron job. + +#### Returns + +`Promise`\<`CronCreateResponse`\> + +*** + +### createForThread() + +> **createForThread**(`threadId`, `assistantId`, `payload`?): `Promise`\<`CronCreateForThreadResponse`\> + +Defined in: [client.ts:342](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L342) + +#### Parameters + +##### threadId + +`string` + +The ID of the thread. + +##### assistantId + +`string` + +Assistant ID to use for this cron job. + +##### payload? + +`CronsCreatePayload` + +Payload for creating a cron job. + +#### Returns + +`Promise`\<`CronCreateForThreadResponse`\> + +The created background run. + +*** + +### delete() + +> **delete**(`cronId`): `Promise`\<`void`\> + +Defined in: [client.ts:402](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L402) + +#### Parameters + +##### cronId + +`string` + +Cron ID of Cron job to delete. + +#### Returns + +`Promise`\<`void`\> + +*** + +### search() + +> **search**(`query`?): `Promise`\<`Cron`[]\> + +Defined in: [client.ts:413](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L413) + +#### Parameters + +##### query? + +Query options. + +###### assistantId? + +`string` + +###### limit? + +`number` + +###### offset? + +`number` + +###### threadId? + +`string` + +#### Returns + +`Promise`\<`Cron`[]\> + +List of crons. diff --git a/libs/sdk-js/docs/classes/RunsClient.md b/libs/sdk-js/docs/classes/RunsClient.md new file mode 100644 index 000000000..15f666453 --- /dev/null +++ b/libs/sdk-js/docs/classes/RunsClient.md @@ -0,0 +1,469 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / RunsClient + +# Class: RunsClient\ + +Defined in: [client.ts:912](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L912) + +## Extends + +- `BaseClient` + +## Type Parameters + +• **TStateType** = `DefaultValues` + +• **TUpdateType** = `TStateType` + +• **TCustomEventType** = `unknown` + +## Constructors + +### new RunsClient() + +> **new RunsClient**\<`TStateType`, `TUpdateType`, `TCustomEventType`\>(`config`?): [`RunsClient`](RunsClient.md)\<`TStateType`, `TUpdateType`, `TCustomEventType`\> + +Defined in: [client.ts:183](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L183) + +#### Parameters + +##### config? + +[`ClientConfig`](../interfaces/ClientConfig.md) + +#### Returns + +[`RunsClient`](RunsClient.md)\<`TStateType`, `TUpdateType`, `TCustomEventType`\> + +#### Inherited from + +`BaseClient.constructor` + +## Methods + +### cancel() + +> **cancel**(`threadId`, `runId`, `wait`, `action`): `Promise`\<`void`\> + +Defined in: [client.ts:1227](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1227) + +Cancel a run. + +#### Parameters + +##### threadId + +`string` + +The ID of the thread. + +##### runId + +`string` + +The ID of the run. + +##### wait + +`boolean` = `false` + +Whether to block when canceling + +##### action + +`CancelAction` = `"interrupt"` + +Action to take when cancelling the run. Possible values are `interrupt` or `rollback`. Default is `interrupt`. + +#### Returns + +`Promise`\<`void`\> + +*** + +### create() + +> **create**(`threadId`, `assistantId`, `payload`?): `Promise`\<`Run`\> + +Defined in: [client.ts:1026](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1026) + +Create a run. + +#### Parameters + +##### threadId + +`string` + +The ID of the thread. + +##### assistantId + +`string` + +Assistant ID to use for this run. + +##### payload? + +`RunsCreatePayload` + +Payload for creating a run. + +#### Returns + +`Promise`\<`Run`\> + +The created run. + +*** + +### createBatch() + +> **createBatch**(`payloads`): `Promise`\<`Run`[]\> + +Defined in: [client.ts:1076](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1076) + +Create a batch of stateless background runs. + +#### Parameters + +##### payloads + +`RunsCreatePayload` & `object`[] + +An array of payloads for creating runs. + +#### Returns + +`Promise`\<`Run`[]\> + +An array of created runs. + +*** + +### delete() + +> **delete**(`threadId`, `runId`): `Promise`\<`void`\> + +Defined in: [client.ts:1329](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1329) + +Delete a run. + +#### Parameters + +##### threadId + +`string` + +The ID of the thread. + +##### runId + +`string` + +The ID of the run. + +#### Returns + +`Promise`\<`void`\> + +*** + +### get() + +> **get**(`threadId`, `runId`): `Promise`\<`Run`\> + +Defined in: [client.ts:1214](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1214) + +Get a run by ID. + +#### Parameters + +##### threadId + +`string` + +The ID of the thread. + +##### runId + +`string` + +The ID of the run. + +#### Returns + +`Promise`\<`Run`\> + +The run. + +*** + +### join() + +> **join**(`threadId`, `runId`, `options`?): `Promise`\<`void`\> + +Defined in: [client.ts:1249](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1249) + +Block until a run is done. + +#### Parameters + +##### threadId + +`string` + +The ID of the thread. + +##### runId + +`string` + +The ID of the run. + +##### options? + +###### signal? + +`AbortSignal` + +#### Returns + +`Promise`\<`void`\> + +*** + +### joinStream() + +> **joinStream**(`threadId`, `runId`, `options`?): `AsyncGenerator`\<\{ `data`: `any`; `event`: `StreamEvent`; `id`: `string`; \}\> + +Defined in: [client.ts:1274](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1274) + +Stream output from a run in real-time, until the run is done. + +#### Parameters + +##### threadId + +The ID of the thread. Can be set to `null` | `undefined` for stateless runs. + +`undefined` | `null` | `string` + +##### runId + +`string` + +The ID of the run. + +##### options? + +Additional options for controlling the stream behavior: + - signal: An AbortSignal that can be used to cancel the stream request + - lastEventId: The ID of the last event received. Can be used to reconnect to a stream without losing events. + - cancelOnDisconnect: When true, automatically cancels the run if the client disconnects from the stream + - streamMode: Controls what types of events to receive from the stream (can be a single mode or array of modes) + Must be a subset of the stream modes passed when creating the run. Background runs default to having the union of all + stream modes enabled. + +`AbortSignal` | \{ `cancelOnDisconnect`: `boolean`; `lastEventId`: `string`; `signal`: `AbortSignal`; `streamMode`: `StreamMode` \| `StreamMode`[]; \} + +#### Returns + +`AsyncGenerator`\<\{ `data`: `any`; `event`: `StreamEvent`; `id`: `string`; \}\> + +An async generator yielding stream parts. + +*** + +### list() + +> **list**(`threadId`, `options`?): `Promise`\<`Run`[]\> + +Defined in: [client.ts:1177](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1177) + +List all runs for a thread. + +#### Parameters + +##### threadId + +`string` + +The ID of the thread. + +##### options? + +Filtering and pagination options. + +###### limit? + +`number` + +Maximum number of runs to return. +Defaults to 10 + +###### offset? + +`number` + +Offset to start from. +Defaults to 0. + +###### status? + +`RunStatus` + +Status of the run to filter by. + +#### Returns + +`Promise`\<`Run`[]\> + +List of runs. + +*** + +### stream() + +Create a run and stream the results. + +#### Param + +The ID of the thread. + +#### Param + +Assistant ID to use for this run. + +#### Param + +Payload for creating a run. + +#### Call Signature + +> **stream**\<`TStreamMode`, `TSubgraphs`\>(`threadId`, `assistantId`, `payload`?): `TypedAsyncGenerator`\<`TStreamMode`, `TSubgraphs`, `TStateType`, `TUpdateType`, `TCustomEventType`\> + +Defined in: [client.ts:917](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L917) + +##### Type Parameters + +• **TStreamMode** *extends* `StreamMode` \| `StreamMode`[] = `StreamMode` + +• **TSubgraphs** *extends* `boolean` = `false` + +##### Parameters + +###### threadId + +`null` + +###### assistantId + +`string` + +###### payload? + +`Omit`\<`RunsStreamPayload`\<`TStreamMode`, `TSubgraphs`\>, `"multitaskStrategy"` \| `"onCompletion"`\> + +##### Returns + +`TypedAsyncGenerator`\<`TStreamMode`, `TSubgraphs`, `TStateType`, `TUpdateType`, `TCustomEventType`\> + +#### Call Signature + +> **stream**\<`TStreamMode`, `TSubgraphs`\>(`threadId`, `assistantId`, `payload`?): `TypedAsyncGenerator`\<`TStreamMode`, `TSubgraphs`, `TStateType`, `TUpdateType`, `TCustomEventType`\> + +Defined in: [client.ts:935](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L935) + +##### Type Parameters + +• **TStreamMode** *extends* `StreamMode` \| `StreamMode`[] = `StreamMode` + +• **TSubgraphs** *extends* `boolean` = `false` + +##### Parameters + +###### threadId + +`string` + +###### assistantId + +`string` + +###### payload? + +`RunsStreamPayload`\<`TStreamMode`, `TSubgraphs`\> + +##### Returns + +`TypedAsyncGenerator`\<`TStreamMode`, `TSubgraphs`, `TStateType`, `TUpdateType`, `TCustomEventType`\> + +*** + +### wait() + +Create a run and wait for it to complete. + +#### Param + +The ID of the thread. + +#### Param + +Assistant ID to use for this run. + +#### Param + +Payload for creating a run. + +#### Call Signature + +> **wait**(`threadId`, `assistantId`, `payload`?): `Promise`\<`DefaultValues`\> + +Defined in: [client.ts:1093](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1093) + +##### Parameters + +###### threadId + +`null` + +###### assistantId + +`string` + +###### payload? + +`Omit`\<`RunsWaitPayload`, `"multitaskStrategy"` \| `"onCompletion"`\> + +##### Returns + +`Promise`\<`DefaultValues`\> + +#### Call Signature + +> **wait**(`threadId`, `assistantId`, `payload`?): `Promise`\<`DefaultValues`\> + +Defined in: [client.ts:1099](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1099) + +##### Parameters + +###### threadId + +`string` + +###### assistantId + +`string` + +###### payload? + +`RunsWaitPayload` + +##### Returns + +`Promise`\<`DefaultValues`\> diff --git a/libs/sdk-js/docs/classes/StoreClient.md b/libs/sdk-js/docs/classes/StoreClient.md new file mode 100644 index 000000000..97e223da3 --- /dev/null +++ b/libs/sdk-js/docs/classes/StoreClient.md @@ -0,0 +1,314 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / StoreClient + +# Class: StoreClient + +Defined in: [client.ts:1347](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1347) + +## Extends + +- `BaseClient` + +## Constructors + +### new StoreClient() + +> **new StoreClient**(`config`?): [`StoreClient`](StoreClient.md) + +Defined in: [client.ts:183](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L183) + +#### Parameters + +##### config? + +[`ClientConfig`](../interfaces/ClientConfig.md) + +#### Returns + +[`StoreClient`](StoreClient.md) + +#### Inherited from + +`BaseClient.constructor` + +## Methods + +### deleteItem() + +> **deleteItem**(`namespace`, `key`): `Promise`\<`void`\> + +Defined in: [client.ts:1468](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1468) + +Delete an item. + +#### Parameters + +##### namespace + +`string`[] + +A list of strings representing the namespace path. + +##### key + +`string` + +The unique identifier for the item. + +#### Returns + +`Promise`\<`void`\> + +Promise + +*** + +### getItem() + +> **getItem**(`namespace`, `key`, `options`?): `Promise`\<`null` \| `Item`\> + +Defined in: [client.ts:1424](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1424) + +Retrieve a single item. + +#### Parameters + +##### namespace + +`string`[] + +A list of strings representing the namespace path. + +##### key + +`string` + +The unique identifier for the item. + +##### options? + +###### refreshTtl? + +`null` \| `boolean` + +Whether to refresh the TTL on this read operation. If null, uses the store's default behavior. + +#### Returns + +`Promise`\<`null` \| `Item`\> + +Promise + +#### Example + +```typescript +const item = await client.store.getItem( + ["documents", "user123"], + "item456", + { refreshTtl: true } +); +console.log(item); +// { +// namespace: ["documents", "user123"], +// key: "item456", +// value: { title: "My Document", content: "Hello World" }, +// createdAt: "2024-07-30T12:00:00Z", +// updatedAt: "2024-07-30T12:00:00Z" +// } +``` + +*** + +### listNamespaces() + +> **listNamespaces**(`options`?): `Promise`\<`ListNamespaceResponse`\> + +Defined in: [client.ts:1564](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1564) + +List namespaces with optional match conditions. + +#### Parameters + +##### options? + +###### limit? + +`number` + +Maximum number of namespaces to return (default is 100). + +###### maxDepth? + +`number` + +Optional integer specifying the maximum depth of namespaces to return. + +###### offset? + +`number` + +Number of namespaces to skip before returning results (default is 0). + +###### prefix? + +`string`[] + +Optional list of strings representing the prefix to filter namespaces. + +###### suffix? + +`string`[] + +Optional list of strings representing the suffix to filter namespaces. + +#### Returns + +`Promise`\<`ListNamespaceResponse`\> + +Promise + +*** + +### putItem() + +> **putItem**(`namespace`, `key`, `value`, `options`?): `Promise`\<`void`\> + +Defined in: [client.ts:1368](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1368) + +Store or update an item. + +#### Parameters + +##### namespace + +`string`[] + +A list of strings representing the namespace path. + +##### key + +`string` + +The unique identifier for the item within the namespace. + +##### value + +`Record`\<`string`, `any`\> + +A dictionary containing the item's data. + +##### options? + +###### index? + +`null` \| `false` \| `string`[] + +Controls search indexing - null (use defaults), false (disable), or list of field paths to index. + +###### ttl? + +`null` \| `number` + +Optional time-to-live in minutes for the item, or null for no expiration. + +#### Returns + +`Promise`\<`void`\> + +Promise + +#### Example + +```typescript +await client.store.putItem( + ["documents", "user123"], + "item456", + { title: "My Document", content: "Hello World" }, + { ttl: 60 } // expires in 60 minutes +); +``` + +*** + +### searchItems() + +> **searchItems**(`namespacePrefix`, `options`?): `Promise`\<`SearchItemsResponse`\> + +Defined in: [client.ts:1519](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1519) + +Search for items within a namespace prefix. + +#### Parameters + +##### namespacePrefix + +`string`[] + +List of strings representing the namespace prefix. + +##### options? + +###### filter? + +`Record`\<`string`, `any`\> + +Optional dictionary of key-value pairs to filter results. + +###### limit? + +`number` + +Maximum number of items to return (default is 10). + +###### offset? + +`number` + +Number of items to skip before returning results (default is 0). + +###### query? + +`string` + +Optional search query. + +###### refreshTtl? + +`null` \| `boolean` + +Whether to refresh the TTL on items returned by this search. If null, uses the store's default behavior. + +#### Returns + +`Promise`\<`SearchItemsResponse`\> + +Promise + +#### Example + +```typescript +const results = await client.store.searchItems( + ["documents"], + { + filter: { author: "John Doe" }, + limit: 5, + refreshTtl: true + } +); +console.log(results); +// { +// items: [ +// { +// namespace: ["documents", "user123"], +// key: "item789", +// value: { title: "Another Document", author: "John Doe" }, +// createdAt: "2024-07-30T12:00:00Z", +// updatedAt: "2024-07-30T12:00:00Z" +// }, +// // ... additional items ... +// ] +// } +``` diff --git a/libs/sdk-js/docs/classes/ThreadsClient.md b/libs/sdk-js/docs/classes/ThreadsClient.md new file mode 100644 index 000000000..7dc422c98 --- /dev/null +++ b/libs/sdk-js/docs/classes/ThreadsClient.md @@ -0,0 +1,433 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / ThreadsClient + +# Class: ThreadsClient\ + +Defined in: [client.ts:626](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L626) + +## Extends + +- `BaseClient` + +## Type Parameters + +• **TStateType** = `DefaultValues` + +• **TUpdateType** = `TStateType` + +## Constructors + +### new ThreadsClient() + +> **new ThreadsClient**\<`TStateType`, `TUpdateType`\>(`config`?): [`ThreadsClient`](ThreadsClient.md)\<`TStateType`, `TUpdateType`\> + +Defined in: [client.ts:183](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L183) + +#### Parameters + +##### config? + +[`ClientConfig`](../interfaces/ClientConfig.md) + +#### Returns + +[`ThreadsClient`](ThreadsClient.md)\<`TStateType`, `TUpdateType`\> + +#### Inherited from + +`BaseClient.constructor` + +## Methods + +### copy() + +> **copy**(`threadId`): `Promise`\<`Thread`\<`TStateType`\>\> + +Defined in: [client.ts:703](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L703) + +Copy an existing thread + +#### Parameters + +##### threadId + +`string` + +ID of the thread to be copied + +#### Returns + +`Promise`\<`Thread`\<`TStateType`\>\> + +Newly copied thread + +*** + +### create() + +> **create**(`payload`?): `Promise`\<`Thread`\<`TStateType`\>\> + +Defined in: [client.ts:648](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L648) + +Create a new thread. + +#### Parameters + +##### payload? + +Payload for creating a thread. + +###### graphId? + +`string` + +Graph ID to associate with the thread. + +###### ifExists? + +`OnConflictBehavior` + +How to handle duplicate creation. + +**Default** + +```ts +"raise" +``` + +###### metadata? + +`Metadata` + +Metadata for the thread. + +###### supersteps? + +`object`[] + +Apply a list of supersteps when creating a thread, each containing a sequence of updates. + +Used for copying a thread between deployments. + +###### threadId? + +`string` + +ID of the thread to create. + +If not provided, a random UUID will be generated. + +#### Returns + +`Promise`\<`Thread`\<`TStateType`\>\> + +The created thread. + +*** + +### delete() + +> **delete**(`threadId`): `Promise`\<`void`\> + +Defined in: [client.ts:736](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L736) + +Delete a thread. + +#### Parameters + +##### threadId + +`string` + +ID of the thread. + +#### Returns + +`Promise`\<`void`\> + +*** + +### get() + +> **get**\<`ValuesType`\>(`threadId`): `Promise`\<`Thread`\<`ValuesType`\>\> + +Defined in: [client.ts:636](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L636) + +Get a thread by ID. + +#### Type Parameters + +• **ValuesType** = `TStateType` + +#### Parameters + +##### threadId + +`string` + +ID of the thread. + +#### Returns + +`Promise`\<`Thread`\<`ValuesType`\>\> + +The thread. + +*** + +### getHistory() + +> **getHistory**\<`ValuesType`\>(`threadId`, `options`?): `Promise`\<`ThreadState`\<`ValuesType`\>[]\> + +Defined in: [client.ts:888](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L888) + +Get all past states for a thread. + +#### Type Parameters + +• **ValuesType** = `TStateType` + +#### Parameters + +##### threadId + +`string` + +ID of the thread. + +##### options? + +Additional options. + +###### before? + +`Config` + +###### checkpoint? + +`Partial`\<`Omit`\<`Checkpoint`, `"thread_id"`\>\> + +###### limit? + +`number` + +###### metadata? + +`Metadata` + +#### Returns + +`Promise`\<`ThreadState`\<`ValuesType`\>[]\> + +List of thread states. + +*** + +### getState() + +> **getState**\<`ValuesType`\>(`threadId`, `checkpoint`?, `options`?): `Promise`\<`ThreadState`\<`ValuesType`\>\> + +Defined in: [client.ts:795](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L795) + +Get state for a thread. + +#### Type Parameters + +• **ValuesType** = `TStateType` + +#### Parameters + +##### threadId + +`string` + +ID of the thread. + +##### checkpoint? + +`string` | `Checkpoint` + +##### options? + +###### subgraphs? + +`boolean` + +#### Returns + +`Promise`\<`ThreadState`\<`ValuesType`\>\> + +Thread state. + +*** + +### patchState() + +> **patchState**(`threadIdOrConfig`, `metadata`): `Promise`\<`void`\> + +Defined in: [client.ts:858](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L858) + +Patch the metadata of a thread. + +#### Parameters + +##### threadIdOrConfig + +Thread ID or config to patch the state of. + +`string` | `Config` + +##### metadata + +`Metadata` + +Metadata to patch the state with. + +#### Returns + +`Promise`\<`void`\> + +*** + +### search() + +> **search**\<`ValuesType`\>(`query`?): `Promise`\<`Thread`\<`ValuesType`\>[]\> + +Defined in: [client.ts:748](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L748) + +List threads + +#### Type Parameters + +• **ValuesType** = `TStateType` + +#### Parameters + +##### query? + +Query options + +###### limit? + +`number` + +Maximum number of threads to return. +Defaults to 10 + +###### metadata? + +`Metadata` + +Metadata to filter threads by. + +###### offset? + +`number` + +Offset to start from. + +###### sortBy? + +`ThreadSortBy` + +Sort by. + +###### sortOrder? + +`SortOrder` + +Sort order. +Must be one of 'asc' or 'desc'. + +###### status? + +`ThreadStatus` + +Thread status to filter on. + +#### Returns + +`Promise`\<`Thread`\<`ValuesType`\>[]\> + +List of threads + +*** + +### update() + +> **update**(`threadId`, `payload`?): `Promise`\<`Thread`\<`DefaultValues`\>\> + +Defined in: [client.ts:716](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L716) + +Update a thread. + +#### Parameters + +##### threadId + +`string` + +ID of the thread. + +##### payload? + +Payload for updating the thread. + +###### metadata? + +`Metadata` + +Metadata for the thread. + +#### Returns + +`Promise`\<`Thread`\<`DefaultValues`\>\> + +The updated thread. + +*** + +### updateState() + +> **updateState**\<`ValuesType`\>(`threadId`, `options`): `Promise`\<`Pick`\<`Config`, `"configurable"`\>\> + +Defined in: [client.ts:829](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L829) + +Add state to a thread. + +#### Type Parameters + +• **ValuesType** = `TUpdateType` + +#### Parameters + +##### threadId + +`string` + +The ID of the thread. + +##### options + +###### asNode? + +`string` + +###### checkpoint? + +`Checkpoint` + +###### checkpointId? + +`string` + +###### values + +`ValuesType` + +#### Returns + +`Promise`\<`Pick`\<`Config`, `"configurable"`\>\> diff --git a/libs/sdk-js/docs/functions/getApiKey.md b/libs/sdk-js/docs/functions/getApiKey.md new file mode 100644 index 000000000..8c8e4f1fa --- /dev/null +++ b/libs/sdk-js/docs/functions/getApiKey.md @@ -0,0 +1,32 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / getApiKey + +# Function: getApiKey() + +> **getApiKey**(`apiKey`?): `undefined` \| `string` + +Defined in: [client.ts:122](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L122) + +Get the API key from the environment. +Precedence: + 1. explicit argument + 2. LANGGRAPH_API_KEY + 3. LANGSMITH_API_KEY + 4. LANGCHAIN_API_KEY + +## Parameters + +### apiKey? + +`string` + +Optional API key provided as an argument + +## Returns + +`undefined` \| `string` + +The API key if found, otherwise undefined diff --git a/libs/sdk-js/docs/interfaces/ClientConfig.md b/libs/sdk-js/docs/interfaces/ClientConfig.md new file mode 100644 index 000000000..38c402010 --- /dev/null +++ b/libs/sdk-js/docs/interfaces/ClientConfig.md @@ -0,0 +1,57 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / ClientConfig + +# Interface: ClientConfig + +Defined in: [client.ts:163](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L163) + +## Properties + +### apiKey? + +> `optional` **apiKey**: `string` + +Defined in: [client.ts:165](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L165) + +*** + +### apiUrl? + +> `optional` **apiUrl**: `string` + +Defined in: [client.ts:164](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L164) + +*** + +### callerOptions? + +> `optional` **callerOptions**: `AsyncCallerParams` + +Defined in: [client.ts:166](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L166) + +*** + +### defaultHeaders? + +> `optional` **defaultHeaders**: `Record`\<`string`, `HeaderValue`\> + +Defined in: [client.ts:168](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L168) + +*** + +### onRequest? + +> `optional` **onRequest**: [`RequestHook`](../type-aliases/RequestHook.md) + +Defined in: [client.ts:169](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L169) + +*** + +### timeoutMs? + +> `optional` **timeoutMs**: `number` + +Defined in: [client.ts:167](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L167) diff --git a/libs/sdk-js/docs/react/README.md b/libs/sdk-js/docs/react/README.md new file mode 100644 index 000000000..0933ca79b --- /dev/null +++ b/libs/sdk-js/docs/react/README.md @@ -0,0 +1,18 @@ +**@langchain/langgraph-sdk** + +*** + +# @langchain/langgraph-sdk/react + +## Interfaces + +- [UseStream](interfaces/UseStream.md) +- [UseStreamOptions](interfaces/UseStreamOptions.md) + +## Type Aliases + +- [MessageMetadata](type-aliases/MessageMetadata.md) + +## Functions + +- [useStream](functions/useStream.md) diff --git a/libs/sdk-js/docs/react/functions/useStream.md b/libs/sdk-js/docs/react/functions/useStream.md new file mode 100644 index 000000000..a9cb7d468 --- /dev/null +++ b/libs/sdk-js/docs/react/functions/useStream.md @@ -0,0 +1,27 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / useStream + +# Function: useStream() + +> **useStream**\<`StateType`, `Bag`\>(`options`): [`UseStream`](../interfaces/UseStream.md)\<`StateType`, `Bag`\> + +Defined in: [react/stream.tsx:652](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L652) + +## Type Parameters + +• **StateType** *extends* `Record`\<`string`, `unknown`\> = `Record`\<`string`, `unknown`\> + +• **Bag** *extends* `object` = `BagTemplate` + +## Parameters + +### options + +[`UseStreamOptions`](../interfaces/UseStreamOptions.md)\<`StateType`, `Bag`\> + +## Returns + +[`UseStream`](../interfaces/UseStream.md)\<`StateType`, `Bag`\> diff --git a/libs/sdk-js/docs/react/interfaces/UseStream.md b/libs/sdk-js/docs/react/interfaces/UseStream.md new file mode 100644 index 000000000..06d1c4375 --- /dev/null +++ b/libs/sdk-js/docs/react/interfaces/UseStream.md @@ -0,0 +1,227 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / UseStream + +# Interface: UseStream\ + +Defined in: [react/stream.tsx:529](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L529) + +## Type Parameters + +• **StateType** *extends* `Record`\<`string`, `unknown`\> = `Record`\<`string`, `unknown`\> + +• **Bag** *extends* `BagTemplate` = `BagTemplate` + +## Properties + +### assistantId + +> **assistantId**: `string` + +Defined in: [react/stream.tsx:614](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L614) + +The ID of the assistant to use. + +*** + +### branch + +> **branch**: `string` + +Defined in: [react/stream.tsx:564](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L564) + +The current branch of the thread. + +*** + +### client + +> **client**: `Client` + +Defined in: [react/stream.tsx:609](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L609) + +LangGraph SDK client used to send request and receive responses. + +*** + +### error + +> **error**: `unknown` + +Defined in: [react/stream.tsx:541](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L541) + +Last seen error from the thread or during streaming. + +*** + +### experimental\_branchTree + +> **experimental\_branchTree**: `Sequence`\<`StateType`\> + +Defined in: [react/stream.tsx:580](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L580) + +**`Experimental`** + +Tree of all branches for the thread. + +*** + +### getMessagesMetadata() + +> **getMessagesMetadata**: (`message`, `index`?) => `undefined` \| [`MessageMetadata`](../type-aliases/MessageMetadata.md)\<`StateType`\> + +Defined in: [react/stream.tsx:601](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L601) + +Get the metadata for a message, such as first thread state the message +was seen in and branch information. + +#### Parameters + +##### message + +`Message` + +The message to get the metadata for. + +##### index? + +`number` + +The index of the message in the thread. + +#### Returns + +`undefined` \| [`MessageMetadata`](../type-aliases/MessageMetadata.md)\<`StateType`\> + +The metadata for the message. + +*** + +### history + +> **history**: `ThreadState`\<`StateType`\>[] + +Defined in: [react/stream.tsx:574](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L574) + +Flattened history of thread states of a thread. + +*** + +### interrupt + +> **interrupt**: `undefined` \| `Interrupt`\<`GetInterruptType`\<`Bag`\>\> + +Defined in: [react/stream.tsx:585](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L585) + +Get the interrupt value for the stream if interrupted. + +*** + +### isLoading + +> **isLoading**: `boolean` + +Defined in: [react/stream.tsx:546](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L546) + +Whether the stream is currently running. + +*** + +### joinStream() + +> **joinStream**: (`runId`) => `Promise`\<`void`\> + +Defined in: [react/stream.tsx:619](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L619) + +Join an active stream. + +#### Parameters + +##### runId + +`string` + +#### Returns + +`Promise`\<`void`\> + +*** + +### messages + +> **messages**: `Message`[] + +Defined in: [react/stream.tsx:591](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L591) + +Messages inferred from the thread. +Will automatically update with incoming message chunks. + +*** + +### setBranch() + +> **setBranch**: (`branch`) => `void` + +Defined in: [react/stream.tsx:569](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L569) + +Set the branch of the thread. + +#### Parameters + +##### branch + +`string` + +#### Returns + +`void` + +*** + +### stop() + +> **stop**: () => `void` + +Defined in: [react/stream.tsx:551](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L551) + +Stops the stream. + +#### Returns + +`void` + +*** + +### submit() + +> **submit**: (`values`, `options`?) => `void` + +Defined in: [react/stream.tsx:556](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L556) + +Create and stream a run to the thread. + +#### Parameters + +##### values + +`undefined` | `null` | `GetUpdateType`\<`Bag`, `StateType`\> + +##### options? + +`SubmitOptions`\<`StateType`, `GetConfigurableType`\<`Bag`\>\> + +#### Returns + +`void` + +*** + +### values + +> **values**: `StateType` + +Defined in: [react/stream.tsx:536](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L536) + +The current values of the thread. diff --git a/libs/sdk-js/docs/react/interfaces/UseStreamOptions.md b/libs/sdk-js/docs/react/interfaces/UseStreamOptions.md new file mode 100644 index 000000000..9fbb6f702 --- /dev/null +++ b/libs/sdk-js/docs/react/interfaces/UseStreamOptions.md @@ -0,0 +1,343 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / UseStreamOptions + +# Interface: UseStreamOptions\ + +Defined in: [react/stream.tsx:413](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L413) + +## Type Parameters + +• **StateType** *extends* `Record`\<`string`, `unknown`\> = `Record`\<`string`, `unknown`\> + +• **Bag** *extends* `BagTemplate` = `BagTemplate` + +## Properties + +### apiKey? + +> `optional` **apiKey**: `string` + +Defined in: [react/stream.tsx:435](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L435) + +The API key to use. + +*** + +### apiUrl? + +> `optional` **apiUrl**: `string` + +Defined in: [react/stream.tsx:430](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L430) + +The URL of the API to use. + +*** + +### assistantId + +> **assistantId**: `string` + +Defined in: [react/stream.tsx:420](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L420) + +The ID of the assistant to use. + +*** + +### callerOptions? + +> `optional` **callerOptions**: `AsyncCallerParams` + +Defined in: [react/stream.tsx:440](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L440) + +Custom call options, such as custom fetch implementation. + +*** + +### client? + +> `optional` **client**: `Client`\<`DefaultValues`, `DefaultValues`, `unknown`\> + +Defined in: [react/stream.tsx:425](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L425) + +Client used to send requests. + +*** + +### defaultHeaders? + +> `optional` **defaultHeaders**: `Record`\<`string`, `HeaderValue`\> + +Defined in: [react/stream.tsx:445](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L445) + +Default headers to send with requests. + +*** + +### messagesKey? + +> `optional` **messagesKey**: `string` + +Defined in: [react/stream.tsx:453](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L453) + +Specify the key within the state that contains messages. +Defaults to "messages". + +#### Default + +```ts +"messages" +``` + +*** + +### onCreated()? + +> `optional` **onCreated**: (`run`) => `void` + +Defined in: [react/stream.tsx:471](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L471) + +Callback that is called when a new stream is created. + +#### Parameters + +##### run + +`RunCallbackMeta` + +#### Returns + +`void` + +*** + +### onCustomEvent()? + +> `optional` **onCustomEvent**: (`data`, `options`) => `void` + +Defined in: [react/stream.tsx:483](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L483) + +Callback that is called when a custom event is received. + +#### Parameters + +##### data + +`GetCustomEventType`\<`Bag`\> + +##### options + +###### mutate + +(`update`) => `void` + +#### Returns + +`void` + +*** + +### onDebugEvent()? + +> `optional` **onDebugEvent**: (`data`) => `void` + +Defined in: [react/stream.tsx:507](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L507) + +**`Internal`** + +Callback that is called when a debug event is received. + This API is experimental and subject to change. + +#### Parameters + +##### data + +`unknown` + +#### Returns + +`void` + +*** + +### onError()? + +> `optional` **onError**: (`error`, `run`) => `void` + +Defined in: [react/stream.tsx:458](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L458) + +Callback that is called when an error occurs. + +#### Parameters + +##### error + +`unknown` + +##### run + +`undefined` | `RunCallbackMeta` + +#### Returns + +`void` + +*** + +### onFinish()? + +> `optional` **onFinish**: (`state`, `run`) => `void` + +Defined in: [react/stream.tsx:463](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L463) + +Callback that is called when the stream is finished. + +#### Parameters + +##### state + +`ThreadState`\<`StateType`\> + +##### run + +`undefined` | `RunCallbackMeta` + +#### Returns + +`void` + +*** + +### onLangChainEvent()? + +> `optional` **onLangChainEvent**: (`data`) => `void` + +Defined in: [react/stream.tsx:501](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L501) + +Callback that is called when a LangChain event is received. + +#### Parameters + +##### data + +###### data + +`unknown` + +###### event + +`string` & `object` \| `"on_tool_start"` \| `"on_tool_stream"` \| `"on_tool_end"` \| `"on_chat_model_start"` \| `"on_chat_model_stream"` \| `"on_chat_model_end"` \| `"on_llm_start"` \| `"on_llm_stream"` \| `"on_llm_end"` \| `"on_chain_start"` \| `"on_chain_stream"` \| `"on_chain_end"` \| `"on_retriever_start"` \| `"on_retriever_stream"` \| `"on_retriever_end"` \| `"on_prompt_start"` \| `"on_prompt_stream"` \| `"on_prompt_end"` + +###### metadata + +`Record`\<`string`, `unknown`\> + +###### name + +`string` + +###### parent_ids + +`string`[] + +###### run_id + +`string` + +###### tags + +`string`[] + +#### Returns + +`void` + +#### See + +https://langchain-ai.github.io/langgraph/cloud/how-tos/stream_events/#stream-graph-in-events-mode for more details. + +*** + +### onMetadataEvent()? + +> `optional` **onMetadataEvent**: (`data`) => `void` + +Defined in: [react/stream.tsx:495](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L495) + +Callback that is called when a metadata event is received. + +#### Parameters + +##### data + +###### run_id + +`string` + +###### thread_id + +`string` + +#### Returns + +`void` + +*** + +### onThreadId()? + +> `optional` **onThreadId**: (`threadId`) => `void` + +Defined in: [react/stream.tsx:517](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L517) + +Callback that is called when the thread ID is updated (ie when a new thread is created). + +#### Parameters + +##### threadId + +`string` + +#### Returns + +`void` + +*** + +### onUpdateEvent()? + +> `optional` **onUpdateEvent**: (`data`) => `void` + +Defined in: [react/stream.tsx:476](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L476) + +Callback that is called when an update event is received. + +#### Parameters + +##### data + +#### Returns + +`void` + +*** + +### reconnectOnMount? + +> `optional` **reconnectOnMount**: `boolean` \| () => `RunMetadataStorage` + +Defined in: [react/stream.tsx:520](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L520) + +Will reconnect the stream on mount + +*** + +### threadId? + +> `optional` **threadId**: `null` \| `string` + +Defined in: [react/stream.tsx:512](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L512) + +The ID of the thread to fetch history and current values from. diff --git a/libs/sdk-js/docs/react/type-aliases/MessageMetadata.md b/libs/sdk-js/docs/react/type-aliases/MessageMetadata.md new file mode 100644 index 000000000..9496a7913 --- /dev/null +++ b/libs/sdk-js/docs/react/type-aliases/MessageMetadata.md @@ -0,0 +1,42 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / MessageMetadata + +# Type Alias: MessageMetadata\ + +> **MessageMetadata**\<`StateType`\>: `object` + +Defined in: [react/stream.tsx:169](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L169) + +## Type Parameters + +• **StateType** *extends* `Record`\<`string`, `unknown`\> + +## Type declaration + +### branch + +> **branch**: `string` \| `undefined` + +The branch of the message. + +### branchOptions + +> **branchOptions**: `string`[] \| `undefined` + +The list of branches this message is part of. +This is useful for displaying branching controls. + +### firstSeenState + +> **firstSeenState**: `ThreadState`\<`StateType`\> \| `undefined` + +The first thread state the message was seen in. + +### messageId + +> **messageId**: `string` + +The ID of the message used. diff --git a/libs/sdk-js/docs/type-aliases/RequestHook.md b/libs/sdk-js/docs/type-aliases/RequestHook.md new file mode 100644 index 000000000..53d535e2e --- /dev/null +++ b/libs/sdk-js/docs/type-aliases/RequestHook.md @@ -0,0 +1,25 @@ +[**@langchain/langgraph-sdk**](../README.md) + +*** + +[@langchain/langgraph-sdk](../README.md) / RequestHook + +# Type Alias: RequestHook() + +> **RequestHook**: (`url`, `init`) => `Promise`\<`RequestInit`\> \| `RequestInit` + +Defined in: [client.ts:158](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L158) + +## Parameters + +### url + +`URL` + +### init + +`RequestInit` + +## Returns + +`Promise`\<`RequestInit`\> \| `RequestInit` diff --git a/libs/sdk-js/index.cjs b/libs/sdk-js/index.cjs new file mode 100644 index 000000000..c84256f7a --- /dev/null +++ b/libs/sdk-js/index.cjs @@ -0,0 +1 @@ +module.exports = require('./dist/index.cjs'); \ No newline at end of file diff --git a/libs/sdk-js/index.d.cts b/libs/sdk-js/index.d.cts new file mode 100644 index 000000000..485d17256 --- /dev/null +++ b/libs/sdk-js/index.d.cts @@ -0,0 +1 @@ +export * from './dist/index.js' \ No newline at end of file diff --git a/libs/sdk-js/index.d.ts b/libs/sdk-js/index.d.ts new file mode 100644 index 000000000..485d17256 --- /dev/null +++ b/libs/sdk-js/index.d.ts @@ -0,0 +1 @@ +export * from './dist/index.js' \ No newline at end of file diff --git a/libs/sdk-js/index.js b/libs/sdk-js/index.js new file mode 100644 index 000000000..485d17256 --- /dev/null +++ b/libs/sdk-js/index.js @@ -0,0 +1 @@ +export * from './dist/index.js' \ No newline at end of file diff --git a/libs/sdk-js/react.cjs b/libs/sdk-js/react.cjs new file mode 100644 index 000000000..86a38b8c3 --- /dev/null +++ b/libs/sdk-js/react.cjs @@ -0,0 +1 @@ +module.exports = require('./dist/react/index.cjs'); \ No newline at end of file diff --git a/libs/sdk-js/react.d.cts b/libs/sdk-js/react.d.cts new file mode 100644 index 000000000..fd96c4787 --- /dev/null +++ b/libs/sdk-js/react.d.cts @@ -0,0 +1 @@ +export * from './dist/react/index.js' \ No newline at end of file diff --git a/libs/sdk-js/react.d.ts b/libs/sdk-js/react.d.ts new file mode 100644 index 000000000..fd96c4787 --- /dev/null +++ b/libs/sdk-js/react.d.ts @@ -0,0 +1 @@ +export * from './dist/react/index.js' \ No newline at end of file diff --git a/libs/sdk-js/react.js b/libs/sdk-js/react.js new file mode 100644 index 000000000..fd96c4787 --- /dev/null +++ b/libs/sdk-js/react.js @@ -0,0 +1 @@ +export * from './dist/react/index.js' \ No newline at end of file From c9966c4febc681388055a0f91f1e19532c66c8ea Mon Sep 17 00:00:00 2001 From: Andrew Nguonly Date: Mon, 7 Jul 2025 12:39:44 -0400 Subject: [PATCH 07/31] docs: Add section about data region for LGP data plane (#5378) Add section about data region. --- docs/docs/concepts/langgraph_data_plane.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/docs/docs/concepts/langgraph_data_plane.md b/docs/docs/concepts/langgraph_data_plane.md index 0b52b8b6f..5d7473cb6 100644 --- a/docs/docs/concepts/langgraph_data_plane.md +++ b/docs/docs/concepts/langgraph_data_plane.md @@ -50,6 +50,15 @@ Runs in a LangGraph Server may be retried for specific failures (currently only This section describes various features of the data plane. +### Data Region + +!!! info "Only for Cloud SaaS" + Data regions are only applicable for [Cloud SaaS](../concepts/langgraph_cloud.md) deployments. + +Deployments can be created in 2 data regions: US and EU + +The data region for a deployment is implied by the data region of the LangSmith organization where the deployment is created. Deployments and the underlying database for the deployments cannot be migrated between data regions. + ### Autoscaling [`Production` type](../concepts/langgraph_control_plane.md#deployment-types) deployments automatically scale up to 10 containers. Scaling is based on 3 metrics: From 844417591d5368b1f5d69de0c86068ed253da45d Mon Sep 17 00:00:00 2001 From: Michael Li Date: Tue, 8 Jul 2025 05:09:00 +1000 Subject: [PATCH 08/31] fix: remove empty notebook (#5379) --- examples/human_in_the_loop/breakpoints.ipynb | 1 - 1 file changed, 1 deletion(-) delete mode 100644 examples/human_in_the_loop/breakpoints.ipynb diff --git a/examples/human_in_the_loop/breakpoints.ipynb b/examples/human_in_the_loop/breakpoints.ipynb deleted file mode 100644 index 0519ecba6..000000000 --- a/examples/human_in_the_loop/breakpoints.ipynb +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file From 7f57e0097575897feeff72aca9cb3d84f169efd4 Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Mon, 7 Jul 2025 15:10:01 -0400 Subject: [PATCH 09/31] docs: move script into scripts (#5384) --- extract_images.py => docs/_scripts/extract_images.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename extract_images.py => docs/_scripts/extract_images.py (100%) diff --git a/extract_images.py b/docs/_scripts/extract_images.py similarity index 100% rename from extract_images.py rename to docs/_scripts/extract_images.py From 1800df7048510e9dc5330b57236420845dc07310 Mon Sep 17 00:00:00 2001 From: jito Date: Tue, 8 Jul 2025 04:25:06 +0900 Subject: [PATCH 10/31] Fix docstring for _unset_config_context function (#5374) Signed-off-by: jitokim --- libs/langgraph/langgraph/utils/runnable.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/langgraph/langgraph/utils/runnable.py b/libs/langgraph/langgraph/utils/runnable.py index 3ca48aec3..4274c8134 100644 --- a/libs/langgraph/langgraph/utils/runnable.py +++ b/libs/langgraph/langgraph/utils/runnable.py @@ -83,7 +83,7 @@ def _unset_config_context(token: Token[RunnableConfig | None], run: Any = None) """Set the child Runnable config + tracing context. Args: - config: The config to set. + token: The config token to reset. """ var_child_runnable_config.reset(token) if run is not None: From e99f6292c510fb5ee046b17a75a006f3b429447d Mon Sep 17 00:00:00 2001 From: jito Date: Tue, 8 Jul 2025 04:27:09 +0900 Subject: [PATCH 11/31] =?UTF-8?q?Fix=20typo=20in=20StreamMode=20debug=20de?= =?UTF-8?q?scription:=20checlkpoints=20=E2=86=92=20checkpoints=20(#5371)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: jitokim --- libs/langgraph/langgraph/types.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/langgraph/langgraph/types.py b/libs/langgraph/langgraph/types.py index d95c4fd51..095e73c24 100644 --- a/libs/langgraph/langgraph/types.py +++ b/libs/langgraph/langgraph/types.py @@ -59,7 +59,7 @@ StreamMode = Literal[ - `"messages"`: Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes or tasks. - `"checkpoints"`: Emit an event when a checkpoint is created, in the same format as returned by get_state(). - `"tasks"`: Emit events when tasks start and finish, including their results and errors. -- `"debug"`: Emit "checlkpoints" and "tasks" events, for debugging purposes. +- `"debug"`: Emit "checkpoints" and "tasks" events, for debugging purposes. """ StreamWriter = Callable[[Any], None] From 141afa8c6278090555a51db6e425e996bbb229f1 Mon Sep 17 00:00:00 2001 From: Michael Li Date: Tue, 8 Jul 2025 05:54:02 +1000 Subject: [PATCH 12/31] fix: remove unused import in generate_llms_text.py (#5380) --- docs/_scripts/generate_llms_text.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/docs/_scripts/generate_llms_text.py b/docs/_scripts/generate_llms_text.py index ff981834e..a262549c9 100644 --- a/docs/_scripts/generate_llms_text.py +++ b/docs/_scripts/generate_llms_text.py @@ -14,10 +14,7 @@ from mkdocs.structure.pages import Page from pydantic import BaseModel, Field from yaml import SafeLoader -from _scripts.notebook_hooks import ( - _on_page_markdown_with_config, - _apply_conditional_rendering, -) +from _scripts.notebook_hooks import _on_page_markdown_with_config HERE = os.path.dirname(os.path.abspath(__file__)) # Get source directory (parent of HERE / docs) From de6c25689d03f8e2115eb8d06091e0353268d9b6 Mon Sep 17 00:00:00 2001 From: Serhii Polishchuk Date: Mon, 7 Jul 2025 22:00:57 +0200 Subject: [PATCH 13/31] dcos: Fix deprecation of TavilySearch (#5375) Fix deprecation: The class `TavilySearchResults` was deprecated in LangChain 0.3.25 and will be removed in 1.0 --- .../tutorials/multi_agent/hierarchical_agent_teams.ipynb | 6 +++--- .../tutorials/multi_agent/multi-agent-collaboration.ipynb | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb b/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb index 3031fa74a..4222d111e 100644 --- a/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb +++ b/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb @@ -49,7 +49,7 @@ "outputs": [], "source": [ "%%capture --no-stderr\n", - "%pip install -U langgraph langchain_community langchain_anthropic langchain_experimental" + "%pip install -U langgraph langchain_community langchain_anthropic langchain-tavily langchain_experimental" ] }, { @@ -121,10 +121,10 @@ "from typing import Annotated, List\n", "\n", "from langchain_community.document_loaders import WebBaseLoader\n", - "from langchain_community.tools.tavily_search import TavilySearchResults\n", + "from langchain_tavily import TavilySearch\n", "from langchain_core.tools import tool\n", "\n", - "tavily_tool = TavilySearchResults(max_results=5)\n", + "tavily_tool = TavilySearch(max_results=5)\n", "\n", "\n", "@tool\n", diff --git a/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb b/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb index 6bdaf978e..223cb4fb6 100644 --- a/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb +++ b/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb @@ -37,7 +37,7 @@ "outputs": [], "source": [ "%%capture --no-stderr\n", - "%pip install -U langchain_community langchain_anthropic langchain_experimental matplotlib langgraph" + "%pip install -U langchain_community langchain_anthropic langchain-tavily langchain_experimental matplotlib langgraph" ] }, { @@ -92,11 +92,11 @@ "source": [ "from typing import Annotated\n", "\n", - "from langchain_community.tools.tavily_search import TavilySearchResults\n", + "from langchain_tavily import TavilySearch\n", "from langchain_core.tools import tool\n", "from langchain_experimental.utilities import PythonREPL\n", "\n", - "tavily_tool = TavilySearchResults(max_results=5)\n", + "tavily_tool = TavilySearch(max_results=5)\n", "\n", "# Warning: This executes code locally, which can be unsafe when not sandboxed\n", "\n", From f3a7925d864d6af62189f55529c9df3b5ccf1c47 Mon Sep 17 00:00:00 2001 From: jito Date: Tue, 8 Jul 2025 05:09:55 +0900 Subject: [PATCH 14/31] =?UTF-8?q?Fix=20typo:=20funtion=20=E2=86=92=20funct?= =?UTF-8?q?ion=20(#5370)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit fix typos Signed-off-by: jitokim --- libs/langgraph/bench/react_agent.py | 4 ++-- libs/langgraph/tests/__snapshots__/test_large_cases.ambr | 2 +- libs/langgraph/tests/test_large_cases.py | 8 ++++---- libs/langgraph/tests/test_large_cases_async.py | 4 ++-- 4 files changed, 9 insertions(+), 9 deletions(-) diff --git a/libs/langgraph/bench/react_agent.py b/libs/langgraph/bench/react_agent.py index a6f84f2dc..59e60fb95 100644 --- a/libs/langgraph/bench/react_agent.py +++ b/libs/langgraph/bench/react_agent.py @@ -15,7 +15,7 @@ from langgraph.pregel import Pregel def react_agent(n_tools: int, checkpointer: Optional[BaseCheckpointSaver]) -> Pregel: - class FakeFuntionChatModel(FakeMessagesListChatModel): + class FakeFunctionChatModel(FakeMessagesListChatModel): def bind_tools(self, functions: list): return self @@ -40,7 +40,7 @@ def react_agent(n_tools: int, checkpointer: Optional[BaseCheckpointSaver]) -> Pr description="", ) - model = FakeFuntionChatModel( + model = FakeFunctionChatModel( responses=[ AIMessage( content="", diff --git a/libs/langgraph/tests/__snapshots__/test_large_cases.ambr b/libs/langgraph/tests/__snapshots__/test_large_cases.ambr index 56b3fb6aa..3b3b383ff 100644 --- a/libs/langgraph/tests/__snapshots__/test_large_cases.ambr +++ b/libs/langgraph/tests/__snapshots__/test_large_cases.ambr @@ -218,7 +218,7 @@ "id": [ "tests", "test_large_cases", - "FakeFuntionChatModel" + "FakeFunctionChatModel" ], "name": "agent" } diff --git a/libs/langgraph/tests/test_large_cases.py b/libs/langgraph/tests/test_large_cases.py index 81de033ba..e642d07da 100644 --- a/libs/langgraph/tests/test_large_cases.py +++ b/libs/langgraph/tests/test_large_cases.py @@ -2377,7 +2377,7 @@ def test_message_graph( from langchain_core.outputs import ChatGeneration, ChatResult from langchain_core.tools import tool - class FakeFuntionChatModel(FakeMessagesListChatModel): + class FakeFunctionChatModel(FakeMessagesListChatModel): def bind_functions(self, functions: list): return self @@ -2403,7 +2403,7 @@ def test_message_graph( tools = [search_api] - model = FakeFuntionChatModel( + model = FakeFunctionChatModel( responses=[ AIMessage( content="", @@ -3100,7 +3100,7 @@ def test_root_graph( from langchain_core.outputs import ChatGeneration, ChatResult from langchain_core.tools import tool - class FakeFuntionChatModel(FakeMessagesListChatModel): + class FakeFunctionChatModel(FakeMessagesListChatModel): def bind_functions(self, functions: list): return self @@ -3126,7 +3126,7 @@ def test_root_graph( tools = [search_api] - model = FakeFuntionChatModel( + model = FakeFunctionChatModel( responses=[ AIMessage( content="", diff --git a/libs/langgraph/tests/test_large_cases_async.py b/libs/langgraph/tests/test_large_cases_async.py index 824624680..29f92d8b9 100644 --- a/libs/langgraph/tests/test_large_cases_async.py +++ b/libs/langgraph/tests/test_large_cases_async.py @@ -2066,7 +2066,7 @@ async def test_message_graph(async_checkpointer: BaseCheckpointSaver) -> None: from langchain_core.messages import AIMessage, HumanMessage from langchain_core.tools import tool - class FakeFuntionChatModel(FakeMessagesListChatModel): + class FakeFunctionChatModel(FakeMessagesListChatModel): def bind_functions(self, functions: list): return self @@ -2077,7 +2077,7 @@ async def test_message_graph(async_checkpointer: BaseCheckpointSaver) -> None: tools = [search_api] - model = FakeFuntionChatModel( + model = FakeFunctionChatModel( responses=[ AIMessage( content="", From cd58fad69db699d5ee653c8d4df1469efbea4117 Mon Sep 17 00:00:00 2001 From: Lauren Hirata Singh Date: Mon, 7 Jul 2025 17:03:05 -0400 Subject: [PATCH 15/31] docs: feedback edits (#5387) --- docs/_scripts/notebook_hooks.py | 1 + docs/docs/agents/overview.md | 2 +- docs/docs/agents/tools.md | 310 ------------------------------ docs/docs/how-tos/graph-api.md | 2 +- docs/docs/how-tos/tool-calling.md | 2 + docs/overrides/main.html | 9 +- 6 files changed, 12 insertions(+), 314 deletions(-) delete mode 100644 docs/docs/agents/tools.md diff --git a/docs/_scripts/notebook_hooks.py b/docs/_scripts/notebook_hooks.py index 7b7f049eb..db31e41ce 100644 --- a/docs/_scripts/notebook_hooks.py +++ b/docs/_scripts/notebook_hooks.py @@ -72,6 +72,7 @@ REDIRECT_MAP = { "how-tos/pass-config-to-tools.ipynb": "how-tos/tool-calling.ipynb#access-config", "how-tos/pass-run-time-values-to-tools.ipynb": "how-tos/tool-calling.ipynb#read-state", "how-tos/update-state-from-tools.ipynb": "how-tos/tool-calling.ipynb#update-state", + "agents/tools.md": "how-tos/tool-calling.md", # multi-agent how-tos "how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.md#handoffs", "how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.md#use-in-a-multi-agent-system", diff --git a/docs/docs/agents/overview.md b/docs/docs/agents/overview.md index d61b9d6ed..96fef74b3 100644 --- a/docs/docs/agents/overview.md +++ b/docs/docs/agents/overview.md @@ -60,7 +60,7 @@ Use the following tool to visualize the graph generated by and to view an outline of the corresponding code. It allows you to explore the infrastructure of the agent as defined by the presence of: -* [`tools`](../agents/tools.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks. +* [`tools`](../how-tos/tool-calling.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks. * [`pre_model_hook`](../how-tos/create-react-agent-manage-message-history.ipynb): A function that is called before the model is invoked. It can be used to condense messages or perform other preprocessing tasks. * `post_model_hook`: A function that is called after the model is invoked. It can be used to implement guardrails, human-in-the-loop flows, or other postprocessing tasks. * [`response_format`](../agents/agents.md#6-configure-structured-output): A data structure used to constrain the type of the final output, e.g., a `pydantic` `BaseModel`. diff --git a/docs/docs/agents/tools.md b/docs/docs/agents/tools.md deleted file mode 100644 index e9f45effb..000000000 --- a/docs/docs/agents/tools.md +++ /dev/null @@ -1,310 +0,0 @@ ---- -search: - boost: 2 -tags: - - agent -hide: - - tags ---- - -# Tools - -[Tools](https://python.langchain.com/docs/concepts/tools/) are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs. - -You can either [define your own tools](#define-simple-tools) or use [prebuilt integrations](#prebuilt-tools) that LangChain provides. - -## Define simple tools - -You can pass a vanilla function to `create_react_agent` to use as a tool: - -```python -from langgraph.prebuilt import create_react_agent - -def multiply(a: int, b: int) -> int: - """Multiply two numbers.""" - return a * b - -create_react_agent( - model="anthropic:claude-3-7-sonnet", - tools=[multiply] -) -``` - -`create_react_agent` automatically converts vanilla functions to [LangChain tools](https://python.langchain.com/docs/concepts/tools/#tool-interface). - -## Customize tools - -For more control over tool behavior, use the `@tool` decorator: - -```python -# highlight-next-line -from langchain_core.tools import tool - -# highlight-next-line -@tool("multiply_tool", parse_docstring=True) -def multiply(a: int, b: int) -> int: - """Multiply two numbers. - - Args: - a: First operand - b: Second operand - """ - return a * b -``` - -You can also define a custom input schema using Pydantic: - -```python -from pydantic import BaseModel, Field - -class MultiplyInputSchema(BaseModel): - """Multiply two numbers""" - a: int = Field(description="First operand") - b: int = Field(description="Second operand") - -# highlight-next-line -@tool("multiply_tool", args_schema=MultiplyInputSchema) -def multiply(a: int, b: int) -> int: - return a * b -``` - -For additional customization, refer to the [custom tools guide](https://python.langchain.com/docs/how_to/custom_tools/). - -## Hide arguments from the model - -Some tools require runtime-only arguments (e.g., user ID or session context) that should not be controllable by the model. - -You can put these arguments in the `state` or `config` of the agent, and access -this information inside the tool: - -```python -from langgraph.prebuilt import InjectedState -from langgraph.prebuilt.chat_agent_executor import AgentState -from langchain_core.runnables import RunnableConfig - -def my_tool( - # This will be populated by an LLM - tool_arg: str, - # access information that's dynamically updated inside the agent - # highlight-next-line - state: Annotated[AgentState, InjectedState], - # access static data that is passed at agent invocation - # highlight-next-line - config: RunnableConfig, -) -> str: - """My tool.""" - do_something_with_state(state["messages"]) - do_something_with_config(config) - ... -``` - -## Disable parallel tool calling - -Some model providers support executing multiple tools in parallel, but -allow users to disable this feature. - -For supported providers, you can disable parallel tool calling by setting `parallel_tool_calls=False` via the `model.bind_tools()` method: - -```python -from langchain.chat_models import init_chat_model - -def add(a: int, b: int) -> int: - """Add two numbers""" - return a + b - -def multiply(a: int, b: int) -> int: - """Multiply two numbers.""" - return a * b - -model = init_chat_model("anthropic:claude-3-5-sonnet-latest", temperature=0) -tools = [add, multiply] -agent = create_react_agent( - # disable parallel tool calls - # highlight-next-line - model=model.bind_tools(tools, parallel_tool_calls=False), - tools=tools -) - -agent.invoke( - {"messages": [{"role": "user", "content": "what's 3 + 5 and 4 * 7?"}]} -) -``` - -## Return tool results directly - -Use `return_direct=True` to return tool results immediately and stop the agent loop: - -```python -from langchain_core.tools import tool - -# highlight-next-line -@tool(return_direct=True) -def add(a: int, b: int) -> int: - """Add two numbers""" - return a + b - -agent = create_react_agent( - model="anthropic:claude-3-7-sonnet-latest", - tools=[add] -) - -agent.invoke( - {"messages": [{"role": "user", "content": "what's 3 + 5?"}]} -) -``` - -## Force tool use - -To force the agent to use specific tools, you can set the `tool_choice` option in `model.bind_tools()`: - -```python -from langchain_core.tools import tool - -# highlight-next-line -@tool(return_direct=True) -def greet(user_name: str) -> int: - """Greet user.""" - return f"Hello {user_name}!" - -tools = [greet] - -agent = create_react_agent( - # highlight-next-line - model=model.bind_tools(tools, tool_choice={"type": "tool", "name": "greet"}), - tools=tools -) - -agent.invoke( - {"messages": [{"role": "user", "content": "Hi, I am Bob"}]} -) -``` - -!!! Warning "Avoid infinite loops" - - Forcing tool usage without stopping conditions can create infinite loops. Use one of the following safeguards: - - - Mark the tool with [`return_direct=True`](#return-tool-results-directly) to end the loop after execution. - - Set [`recursion_limit`](../concepts/low_level.md#recursion-limit) to restrict the number of execution steps. - -## Handle tool errors - -By default, the agent will catch all exceptions raised during tool calls and will pass those as tool messages to the LLM. To control how the errors are handled, you can use the prebuilt [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] — the node that executes tools inside `create_react_agent` — via its `handle_tool_errors` parameter: - -=== "Enable error handling (default)" - - ```python - from langgraph.prebuilt import create_react_agent - - def multiply(a: int, b: int) -> int: - """Multiply two numbers.""" - if a == 42: - raise ValueError("The ultimate error") - return a * b - - # Run with error handling (default) - agent = create_react_agent( - model="anthropic:claude-3-7-sonnet-latest", - tools=[multiply] - ) - agent.invoke( - {"messages": [{"role": "user", "content": "what's 42 x 7?"}]} - ) - ``` - -=== "Disable error handling" - - ```python - from langgraph.prebuilt import create_react_agent, ToolNode - - def multiply(a: int, b: int) -> int: - """Multiply two numbers.""" - if a == 42: - raise ValueError("The ultimate error") - return a * b - - # highlight-next-line - tool_node = ToolNode( - [multiply], - # highlight-next-line - handle_tool_errors=False # (1)! - ) - agent_no_error_handling = create_react_agent( - model="anthropic:claude-3-7-sonnet-latest", - tools=tool_node - ) - agent_no_error_handling.invoke( - {"messages": [{"role": "user", "content": "what's 42 x 7?"}]} - ) - ``` - - 1. This disables error handling (enabled by default). See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode]. - -=== "Custom error handling" - - ```python - from langgraph.prebuilt import create_react_agent, ToolNode - - def multiply(a: int, b: int) -> int: - """Multiply two numbers.""" - if a == 42: - raise ValueError("The ultimate error") - return a * b - - # highlight-next-line - tool_node = ToolNode( - [multiply], - # highlight-next-line - handle_tool_errors=( - "Can't use 42 as a first operand, you must switch operands!" # (1)! - ) - ) - agent_custom_error_handling = create_react_agent( - model="anthropic:claude-3-7-sonnet-latest", - tools=tool_node - ) - agent_custom_error_handling.invoke( - {"messages": [{"role": "user", "content": "what's 42 x 7?"}]} - ) - ``` - - 1. This provides a custom message to send to the LLM in case of an exception. See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode]. - -See [API reference][langgraph.prebuilt.tool_node.ToolNode] for more information on different tool error handling options. - -## Working with memory - -LangGraph allows access to short-term and long-term memory from tools. See [Memory](../how-tos/memory/add-memory.md) guide for more information on: - -* how to [read](../how-tos/memory/add-memory.md#read-short-term) from and [write](../how-tos/memory/add-memory.md#write-short-term) to **short-term** memory -* how to [read](../how-tos/memory/add-memory.md#read-long-term) from and [write](../how-tos/memory/add-memory.md#write-long-term) to **long-term** memory - -## Prebuilt tools - -You can use prebuilt tools from model providers by passing a dictionary with tool specs to the `tools` parameter of `create_react_agent`. For example, to use the `web_search_preview` tool from OpenAI: - -```python -from langgraph.prebuilt import create_react_agent - -agent = create_react_agent( - model="openai:gpt-4o-mini", - tools=[{"type": "web_search_preview"}] -) -response = agent.invoke( - {"messages": ["What was a positive news story from today?"]} -) -``` - -Additionally, LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development. - -You can browse the full list of available integrations in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/tools/). - -Some commonly used tool categories include: - -- **Search**: Bing, SerpAPI, Tavily -- **Code interpreters**: Python REPL, Node.js REPL -- **Databases**: SQL, MongoDB, Redis -- **Web data**: Web scraping and browsing -- **APIs**: OpenWeatherMap, NewsAPI, and others - -These integrations can be configured and added to your agents using the same `tools` parameter shown in the examples above. - diff --git a/docs/docs/how-tos/graph-api.md b/docs/docs/how-tos/graph-api.md index 2e15cacb1..3cf08f8c5 100644 --- a/docs/docs/how-tos/graph-api.md +++ b/docs/docs/how-tos/graph-api.md @@ -658,7 +658,7 @@ print(graph.invoke({}, {"configurable": {"my_runtime_value": "b"}})) There are many use cases where you may wish for your node to have a custom retry policy, for example if you are calling an API, querying a database, or calling an LLM, etc. LangGraph lets you add retry policies to nodes. -To configure a retry policy, pass the `retry_policy` parameter to the [add_node](https://langchain-ai.github.io/langgraph/reference/graphs.md#langgraph.graph.state.StateGraph.add_node). The `retry_policy` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters and associate it with a node: +To configure a retry policy, pass the `retry_policy` parameter to the [add_node](../reference/graphs.md#langgraph.graph.state.StateGraph.add_node). The `retry_policy` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters and associate it with a node: ```python from langgraph.pregel import RetryPolicy diff --git a/docs/docs/how-tos/tool-calling.md b/docs/docs/how-tos/tool-calling.md index a2e711ba3..9968b0036 100644 --- a/docs/docs/how-tos/tool-calling.md +++ b/docs/docs/how-tos/tool-calling.md @@ -344,6 +344,8 @@ tool_node.invoke({"messages": [...]}) ## Tool customization +For more control over tool behavior, use the `@tool` decorator. + ### Parameter descriptions Auto-generate descriptions from docstrings: diff --git a/docs/overrides/main.html b/docs/overrides/main.html index 12a20dfbb..b3e63c56d 100644 --- a/docs/overrides/main.html +++ b/docs/overrides/main.html @@ -184,7 +184,12 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= visibility: hidden; } - /* Dark mode banner links */ + /* Dark mode banner styling */ + [data-md-color-scheme="slate"] .md-banner { + background-color: #CFC9FA; /* Same light purple for both modes */ + color: #000000; + } + [data-md-color-scheme="slate"] .md-banner a { color: #000000; } @@ -220,5 +225,5 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= {% endblock %} {% block announce %} -We are growing and hiring for multiple roles for LangChain, LangGraph and LangSmith. Join our team! +Our Building Ambient Agents with LangGraph course is now available on LangChain Academy! {% endblock %} From c13c4746266bf41b8091c8758f0ae04349e48499 Mon Sep 17 00:00:00 2001 From: hari-dhanushkodi Date: Mon, 7 Jul 2025 17:44:57 -0400 Subject: [PATCH 16/31] docs: update lgp deployment metric list (#5388) --- docs/docs/concepts/langgraph_control_plane.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/docs/concepts/langgraph_control_plane.md b/docs/docs/concepts/langgraph_control_plane.md index ebc880ce5..006a57df9 100644 --- a/docs/docs/concepts/langgraph_control_plane.md +++ b/docs/docs/concepts/langgraph_control_plane.md @@ -112,6 +112,8 @@ After a deployment is ready, the control plane monitors the deployment and recor - Number of container restarts. - Number of replicas (this will increase with [autoscaling](../concepts/langgraph_data_plane.md#autoscaling)). - [Postgres](../concepts/langgraph_data_plane.md#postgres) CPU, memory usage, and disk usage. +- [LangGraph Server queue](../concepts/langgraph_server.md#persistence-and-task-queue) pending/active run count. +- [LangGraph Server API](../concepts/langgraph_server.md) success response count, error response count, and latency. These metrics are displayed as charts in the Control Plane UI. From 8118e905432748f0cfd85d2d3df0aa3dfcb74a17 Mon Sep 17 00:00:00 2001 From: ccurme Date: Mon, 7 Jul 2025 18:02:02 -0400 Subject: [PATCH 17/31] docs: update sql tutorial (#5389) --- docs/docs/tutorials/sql-agent.ipynb | 335 +++++++++------------------- 1 file changed, 109 insertions(+), 226 deletions(-) diff --git a/docs/docs/tutorials/sql-agent.ipynb b/docs/docs/tutorials/sql-agent.ipynb index 843179305..a06626f40 100644 --- a/docs/docs/tutorials/sql-agent.ipynb +++ b/docs/docs/tutorials/sql-agent.ipynb @@ -264,183 +264,109 @@ "text": [ "================================\u001b[1m Human Message \u001b[0m=================================\n", "\n", - "Which sales agent made the most in sales in 2009?\n", + "Which genre on average has the longest tracks?\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "Tool Calls:\n", - " sql_db_list_tables (call_0MfnhUYmRNVe03m6fYClaoa0)\n", - " Call ID: call_0MfnhUYmRNVe03m6fYClaoa0\n", + " sql_db_list_tables (call_d8lCgywSroCgpVl558nmXKwA)\n", + " Call ID: call_d8lCgywSroCgpVl558nmXKwA\n", " Args:\n", - " tool_input:\n", "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: sql_db_list_tables\n", "\n", "Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "Tool Calls:\n", - " sql_db_schema (call_XwI6uES0HeZ2xCRxkkdWzB3I)\n", - " Call ID: call_XwI6uES0HeZ2xCRxkkdWzB3I\n", + " sql_db_schema (call_nNf6IIUcwMYLIkE0l6uWkZHe)\n", + " Call ID: call_nNf6IIUcwMYLIkE0l6uWkZHe\n", " Args:\n", - " table_names: Employee, Invoice, InvoiceLine\n", + " table_names: Genre, Track\n", "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: sql_db_schema\n", "\n", "\n", - "CREATE TABLE \"Employee\" (\n", - "\t\"EmployeeId\" INTEGER NOT NULL, \n", - "\t\"LastName\" NVARCHAR(20) NOT NULL, \n", - "\t\"FirstName\" NVARCHAR(20) NOT NULL, \n", - "\t\"Title\" NVARCHAR(30), \n", - "\t\"ReportsTo\" INTEGER, \n", - "\t\"BirthDate\" DATETIME, \n", - "\t\"HireDate\" DATETIME, \n", - "\t\"Address\" NVARCHAR(70), \n", - "\t\"City\" NVARCHAR(40), \n", - "\t\"State\" NVARCHAR(40), \n", - "\t\"Country\" NVARCHAR(40), \n", - "\t\"PostalCode\" NVARCHAR(10), \n", - "\t\"Phone\" NVARCHAR(24), \n", - "\t\"Fax\" NVARCHAR(24), \n", - "\t\"Email\" NVARCHAR(60), \n", - "\tPRIMARY KEY (\"EmployeeId\"), \n", - "\tFOREIGN KEY(\"ReportsTo\") REFERENCES \"Employee\" (\"EmployeeId\")\n", + "CREATE TABLE \"Genre\" (\n", + "\t\"GenreId\" INTEGER NOT NULL, \n", + "\t\"Name\" NVARCHAR(120), \n", + "\tPRIMARY KEY (\"GenreId\")\n", ")\n", "\n", "/*\n", - "3 rows from Employee table:\n", - "EmployeeId\tLastName\tFirstName\tTitle\tReportsTo\tBirthDate\tHireDate\tAddress\tCity\tState\tCountry\tPostalCode\tPhone\tFax\tEmail\n", - "1\tAdams\tAndrew\tGeneral Manager\tNone\t1962-02-18 00:00:00\t2002-08-14 00:00:00\t11120 Jasper Ave NW\tEdmonton\tAB\tCanada\tT5K 2N1\t+1 (780) 428-9482\t+1 (780) 428-3457\tandrew@chinookcorp.com\n", - "2\tEdwards\tNancy\tSales Manager\t1\t1958-12-08 00:00:00\t2002-05-01 00:00:00\t825 8 Ave SW\tCalgary\tAB\tCanada\tT2P 2T3\t+1 (403) 262-3443\t+1 (403) 262-3322\tnancy@chinookcorp.com\n", - "3\tPeacock\tJane\tSales Support Agent\t2\t1973-08-29 00:00:00\t2002-04-01 00:00:00\t1111 6 Ave SW\tCalgary\tAB\tCanada\tT2P 5M5\t+1 (403) 262-3443\t+1 (403) 262-6712\tjane@chinookcorp.com\n", + "3 rows from Genre table:\n", + "GenreId\tName\n", + "1\tRock\n", + "2\tJazz\n", + "3\tMetal\n", "*/\n", "\n", "\n", - "CREATE TABLE \"Invoice\" (\n", - "\t\"InvoiceId\" INTEGER NOT NULL, \n", - "\t\"CustomerId\" INTEGER NOT NULL, \n", - "\t\"InvoiceDate\" DATETIME NOT NULL, \n", - "\t\"BillingAddress\" NVARCHAR(70), \n", - "\t\"BillingCity\" NVARCHAR(40), \n", - "\t\"BillingState\" NVARCHAR(40), \n", - "\t\"BillingCountry\" NVARCHAR(40), \n", - "\t\"BillingPostalCode\" NVARCHAR(10), \n", - "\t\"Total\" NUMERIC(10, 2) NOT NULL, \n", - "\tPRIMARY KEY (\"InvoiceId\"), \n", - "\tFOREIGN KEY(\"CustomerId\") REFERENCES \"Customer\" (\"CustomerId\")\n", - ")\n", - "\n", - "/*\n", - "3 rows from Invoice table:\n", - "InvoiceId\tCustomerId\tInvoiceDate\tBillingAddress\tBillingCity\tBillingState\tBillingCountry\tBillingPostalCode\tTotal\n", - "1\t2\t2009-01-01 00:00:00\tTheodor-Heuss-Straße 34\tStuttgart\tNone\tGermany\t70174\t1.98\n", - "2\t4\t2009-01-02 00:00:00\tUllevålsveien 14\tOslo\tNone\tNorway\t0171\t3.96\n", - "3\t8\t2009-01-03 00:00:00\tGrétrystraat 63\tBrussels\tNone\tBelgium\t1000\t5.94\n", - "*/\n", - "\n", - "\n", - "CREATE TABLE \"InvoiceLine\" (\n", - "\t\"InvoiceLineId\" INTEGER NOT NULL, \n", - "\t\"InvoiceId\" INTEGER NOT NULL, \n", + "CREATE TABLE \"Track\" (\n", "\t\"TrackId\" INTEGER NOT NULL, \n", + "\t\"Name\" NVARCHAR(200) NOT NULL, \n", + "\t\"AlbumId\" INTEGER, \n", + "\t\"MediaTypeId\" INTEGER NOT NULL, \n", + "\t\"GenreId\" INTEGER, \n", + "\t\"Composer\" NVARCHAR(220), \n", + "\t\"Milliseconds\" INTEGER NOT NULL, \n", + "\t\"Bytes\" INTEGER, \n", "\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n", - "\t\"Quantity\" INTEGER NOT NULL, \n", - "\tPRIMARY KEY (\"InvoiceLineId\"), \n", - "\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n", - "\tFOREIGN KEY(\"InvoiceId\") REFERENCES \"Invoice\" (\"InvoiceId\")\n", + "\tPRIMARY KEY (\"TrackId\"), \n", + "\tFOREIGN KEY(\"MediaTypeId\") REFERENCES \"MediaType\" (\"MediaTypeId\"), \n", + "\tFOREIGN KEY(\"GenreId\") REFERENCES \"Genre\" (\"GenreId\"), \n", + "\tFOREIGN KEY(\"AlbumId\") REFERENCES \"Album\" (\"AlbumId\")\n", ")\n", "\n", "/*\n", - "3 rows from InvoiceLine table:\n", - "InvoiceLineId\tInvoiceId\tTrackId\tUnitPrice\tQuantity\n", - "1\t1\t2\t0.99\t1\n", - "2\t1\t4\t0.99\t1\n", - "3\t2\t6\t0.99\t1\n", + "3 rows from Track table:\n", + "TrackId\tName\tAlbumId\tMediaTypeId\tGenreId\tComposer\tMilliseconds\tBytes\tUnitPrice\n", + "1\tFor Those About To Rock (We Salute You)\t1\t1\t1\tAngus Young, Malcolm Young, Brian Johnson\t343719\t11170334\t0.99\n", + "2\tBalls to the Wall\t2\t2\t1\tNone\t342562\t5510424\t0.99\n", + "3\tFast As a Shark\t3\t2\t1\tF. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman\t230619\t3990994\t0.99\n", "*/\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "Tool Calls:\n", - " sql_db_schema (call_dHE5FKSycFhYIkdPKtpEk9rM)\n", - " Call ID: call_dHE5FKSycFhYIkdPKtpEk9rM\n", + " sql_db_query_checker (call_urTRmtiGtTxkwHtscec7Fd2K)\n", + " Call ID: call_urTRmtiGtTxkwHtscec7Fd2K\n", " Args:\n", - " table_names: Customer\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: sql_db_schema\n", - "\n", - "\n", - "CREATE TABLE \"Customer\" (\n", - "\t\"CustomerId\" INTEGER NOT NULL, \n", - "\t\"FirstName\" NVARCHAR(40) NOT NULL, \n", - "\t\"LastName\" NVARCHAR(20) NOT NULL, \n", - "\t\"Company\" NVARCHAR(80), \n", - "\t\"Address\" NVARCHAR(70), \n", - "\t\"City\" NVARCHAR(40), \n", - "\t\"State\" NVARCHAR(40), \n", - "\t\"Country\" NVARCHAR(40), \n", - "\t\"PostalCode\" NVARCHAR(10), \n", - "\t\"Phone\" NVARCHAR(24), \n", - "\t\"Fax\" NVARCHAR(24), \n", - "\t\"Email\" NVARCHAR(60) NOT NULL, \n", - "\t\"SupportRepId\" INTEGER, \n", - "\tPRIMARY KEY (\"CustomerId\"), \n", - "\tFOREIGN KEY(\"SupportRepId\") REFERENCES \"Employee\" (\"EmployeeId\")\n", - ")\n", - "\n", - "/*\n", - "3 rows from Customer table:\n", - "CustomerId\tFirstName\tLastName\tCompany\tAddress\tCity\tState\tCountry\tPostalCode\tPhone\tFax\tEmail\tSupportRepId\n", - "1\tLuís\tGonçalves\tEmbraer - Empresa Brasileira de Aeronáutica S.A.\tAv. Brigadeiro Faria Lima, 2170\tSão José dos Campos\tSP\tBrazil\t12227-000\t+55 (12) 3923-5555\t+55 (12) 3923-5566\tluisg@embraer.com.br\t3\n", - "2\tLeonie\tKöhler\tNone\tTheodor-Heuss-Straße 34\tStuttgart\tNone\tGermany\t70174\t+49 0711 2842222\tNone\tleonekohler@surfeu.de\t5\n", - "3\tFrançois\tTremblay\tNone\t1498 rue Bélanger\tMontréal\tQC\tCanada\tH2G 1A7\t+1 (514) 721-4711\tNone\tftremblay@gmail.com\t3\n", - "*/\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " sql_db_query_checker (call_QPtWY5cUZWgWhr13Kz6eBJkU)\n", - " Call ID: call_QPtWY5cUZWgWhr13Kz6eBJkU\n", - " Args:\n", - " query: SELECT e.EmployeeId, e.FirstName, e.LastName, SUM(i.Total) as TotalSales\n", - "FROM Employee e\n", - "JOIN Customer c ON e.EmployeeId = c.SupportRepId\n", - "JOIN Invoice i ON c.CustomerId = i.CustomerId\n", - "WHERE i.InvoiceDate BETWEEN '2009-01-01' AND '2009-12-31'\n", - "GROUP BY e.EmployeeId, e.FirstName, e.LastName\n", - "ORDER BY TotalSales DESC\n", + " query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgMilliseconds\n", + "FROM Track\n", + "JOIN Genre ON Track.GenreId = Genre.GenreId\n", + "GROUP BY Genre.Name\n", + "ORDER BY AvgMilliseconds DESC\n", "LIMIT 1;\n", "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: sql_db_query_checker\n", "\n", "```sql\n", - "SELECT e.EmployeeId, e.FirstName, e.LastName, SUM(i.Total) as TotalSales\n", - "FROM Employee e\n", - "JOIN Customer c ON e.EmployeeId = c.SupportRepId\n", - "JOIN Invoice i ON c.CustomerId = i.CustomerId\n", - "WHERE i.InvoiceDate BETWEEN '2009-01-01' AND '2009-12-31'\n", - "GROUP BY e.EmployeeId, e.FirstName, e.LastName\n", - "ORDER BY TotalSales DESC\n", + "SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgMilliseconds\n", + "FROM Track\n", + "JOIN Genre ON Track.GenreId = Genre.GenreId\n", + "GROUP BY Genre.Name\n", + "ORDER BY AvgMilliseconds DESC\n", "LIMIT 1;\n", "```\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "Tool Calls:\n", - " sql_db_query (call_X747YMElSeY5D3CfhE6KzLUj)\n", - " Call ID: call_X747YMElSeY5D3CfhE6KzLUj\n", + " sql_db_query (call_RNMqyUEMv0rvy0UxSwrXY2AV)\n", + " Call ID: call_RNMqyUEMv0rvy0UxSwrXY2AV\n", " Args:\n", - " query: SELECT e.EmployeeId, e.FirstName, e.LastName, SUM(i.Total) as TotalSales\n", - "FROM Employee e\n", - "JOIN Customer c ON e.EmployeeId = c.SupportRepId\n", - "JOIN Invoice i ON c.CustomerId = i.CustomerId\n", - "WHERE i.InvoiceDate BETWEEN '2009-01-01' AND '2009-12-31'\n", - "GROUP BY e.EmployeeId, e.FirstName, e.LastName\n", - "ORDER BY TotalSales DESC\n", + " query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgMilliseconds\n", + "FROM Track\n", + "JOIN Genre ON Track.GenreId = Genre.GenreId\n", + "GROUP BY Genre.Name\n", + "ORDER BY AvgMilliseconds DESC\n", "LIMIT 1;\n", "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: sql_db_query\n", "\n", - "[(5, 'Steve', 'Johnson', 164.34)]\n", + "[('Sci Fi & Fantasy', 2911783.0384615385)]\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", - "The sales agent who made the most in sales in 2009 was Steve Johnson, with total sales of 164.34.\n" + "The genre with the longest average track length is \"Sci Fi & Fantasy,\" with an average duration of about 2,911,783 milliseconds (approximately 48.5 minutes) per track.\n" ] } ], "source": [ - "question = \"Which sales agent made the most in sales in 2009?\"\n", + "question = \"Which genre on average has the longest tracks?\"\n", "\n", "for step in agent.stream(\n", " {\"messages\": [{\"role\": \"user\", \"content\": question}]},\n", @@ -484,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "992f6ce6-f932-4f44-994c-c0ba5032dbd1", "metadata": {}, "outputs": [], @@ -606,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "8b3ce09d-0eec-456d-8447-d04f79ccfc23", "metadata": {}, "outputs": [], @@ -652,13 +578,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "f728ed5f-7bcd-489e-9174-4e4725962335", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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a1NHDc9nSX0JCTwSfPlZaWtK//1CE0LSpcw8e2r18xY96evq8gAk/9B30VWvOzm4HD5w6FXz0j4O7hcIKRweX9et2slisKus0N7dU/ifUQHCvlFbt0d8FcjnVubsW2YU0D0lPiytKK72H6TXNy0G3FgBMQbcWNIUVKxckVPOVkX79hsyetaDJK2oGIJygKSxZtEpcKa5yEYfNafJymgcIJ2gKOjq6ZJfQ/MCYEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQThbNRUOjfr1tJKgWlQqlaPWdBfVQThbNS0DZnZaRR1WBAghlJtewdWFcIImYd6WIxRIZVL4Tm+dlBSILR3U6rBi44BwtmpUGvIepnf7dCbZhTQDt09ndemvw1ShNNkrwkwIAOWmiy4e+OjcXVtLn8niwBj0C+IKWWGW8OWT4j4TDE3bsJvypSGcACGExELZ8zvF+R9FZcUNnKm9ceXm5enp6TXdTqp66lp0LQOmaw9NjnpT/9mCcAIcde3aNSIiAtsbJTQNGHMCgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJ8BR27ZtKRQc5pQmE4QT4Oj169cw3TmEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMUeBbcwAfvXv3ptPpVCo1KytLT0+PSqUihIyNjQ8fPkx2aSSgk10AAP/Jy8uj0WgIIQqFkp+fjxDicDg8Ho/susgB3VqAEU9Pz6+6cjY2Nn5+fuRVRCYIJ8DIpEmTNDU1FQ9VVVXHjBlDakVkgnACjHTu3NnOzk7x0MbGxt/fn9SKyAThBHiZMGECl8sldputdrRJgHACvHTp0sXW1hYhZGFh0bt3b7LLIRMcrW32ZFJ5flalTCoju5BGM3zA1LwPlSMGTsxOE5JdS6OhUinahkw6ox6T8cJ5zmZMUCx5cLkgOb7Uylm9JL+S7HJATbi6jJQEgUU7Va8BOpp6jLpsAuFsrspLpMHb03uPM9EyYJJdC6irkvzKf059HDbXVEOn9k4rhLNZkkrkf6xIHr/KhuxCQEOEbEset9yCrUareTUIZ7N0/2K+jhHbzF6V7EJAQ2SlVHx4U9prlH7Nq8HR2mYp43W5unadxi0AQ1wdRtrL8lpXg3A2SzQmVUMHhprNFUeDrsqlS8S1rAbhbJYKs0RyGYxHmrGCLBGFUstvEMIJAKYgnABgCsIJAKYgnABgCsIJAKYgnABgCsIJAKYgnABgCsIJAKYgnABgCsIJAKYgnKBq586H+PXuTPw8ZJjfib+a05zrV/++4OPrIZFIyC7ku0A4AZlSUt7zxgwguwpMQTgBmV6/eUl2CfiC2fdai/T01B2/boiLe2FsZNK9e68pk2czmUyE0PkLoY8f309KSmCyWK4u7adOnWtibNqwl7h85VxY2F8lpSWent2mTp7DGzNg1coNvr36IISu37hy+cq5lJR3VlZtevn0Hj5sNIVCOXrsANFb9vH1mDN74cgRY6trWS6Xnzt/+saNqxkf0izMrTw8PKdMnk3cVaW694UQKijIX7fh58TEOFNTc17AhP79hhDPJybGHT9x8NWrRK6mVhfP7hMnzFBVVUUIrQ1aTqFQunh237ZjHY1Gs2/r+MuaLRcvnTl+4qCGBrdP7wGzZs6nUCgIoUeP7kf8eyMu/kVJCb+dvdP48dPc3Twa9qHVAPacrUJ2dta8/012dnLbsX1/QMCE2xHXd+/ZihCKj4/Zs3ebo6NrUND25cvWFhUVbti4qmEvkfQq8dddm7y9/f46fr5nD7+g9SsQQsRtwm7dvr5l61o7W/vgk5enTZ179lzw3n07EEKTJ83iBUwwMDD893ZUDclECJ0/H3Ly1J8jho8JCb46cODwv69dDAk9UcP7QgjR6fTde7eOHzdt544D9vaOu37bnJOTjRD68DFjydI5QpFw756j69ZuT05+u3DRDGJ0SqfTExJjExJjz4SGH9j3V0Ji7PyF02Uy6dXLd9cEbg47c/LJk0iEkFAo3LBplUgkWr5s7cYNu8zNLVeuWlhYWNCwz60GsOdsFc6eC2apqEyeNItGo7V378hkMl+/fokQcnBwPnokzNTUnE6nI4QklZU/r1rIL+FzNbj1fYmbN69qa+tMnjSLTqd7efV48zbp5ct4YtG1axddXNwXzF+OENLS0p48cdbW7UHjxkzR0tKuY+Oxcc/btnXo02cAQmhA/6Hu7h0rystreF8IIYlEMmjgiM6dvBBC+vqGt26FJ71KMDAwvHUrnEFnrFu7ncvVRAgtWbx69NiBDyLv9PT2QwiJxeJ5c5cwGAwuV9Paqo1EKpk8aRZCyN3NQ1NT633yW0/PbioqKocPhrDZbKKFdvZOly6fjU+I8e7hW98PrWYQzlYhOfmtra090Q9ECPXtM7Bvn4EIIRqNlpn54fd9O5JeJZSVlRFLi4sKGxDO5JR37do5ESFHCPXo7nv8xCGEkEwmS0iMnTB+umJNd/eOMpksLv5F3f83Ozm5Hjy0Z+u2IBcX9y5deig63tW9L4KrS3viB02uFkJIJBQihBITY+3tHYlcIYQMDY2MjU3j4l8Q4TQxMWMwPk3OxOZwdLR1Fa2pclQFglLi5/LyssNH9sbERhcU5H/60IqL6vuJ1QrC2SqUlQk0NbW+fT4y8u6qwMVjx0yeOWO+jY1tVPSTpcvmNewlBIJSfX1DxUPF/36xWFxZWXnkz31H/tz3+fpFRYV1b3zE8DEcjmrkw7tbtq6l0+k9e/rPnP6jrq5ede+LoPhLQQwUFXW+ev3Sx/eLIWLR/3dKiX64wlcPCTk52fMXTmvv3mn1yo0ODs4UCsW/j2fd30vdQThbBVVVtbLysm+fv3rtgrOz27Spc4mHij1DA7BYKpLK/2adLyj8tEtRUVHhcDi9/fv3+HI/aWxUj8NOVCp1QP+hA/oPTU1Nfv786bETB8vKBBvX/1rd+6qBto6us7Mb0VlV4GpoVr/F1+7c/UcsFi9ftpbNZitpn0mAcLYKbds6XLl6TiKREDuT2xE3wsMvbdm8p6SEb2hgpFjt/v2IBr+EiYnZ27evFA8jI+8ofraxsSsVlCqOZ1ZWVmZlfdTXN6h74zduXLWza2dlZWNpaW1paV0qKP372oUa3lcNTdlY2978529Xl/aKvWJqarKpqXndiykp4auraxDJRAjdvXe77tvWCxytbRX69xsiFot3/roxKvrJ/Qf/Hjq8R0dXj0ajtbGxexb1+EVMlEQiOXP2FLFydk5WA16iq5d3WlpK8Oljcrn8WdTj+PgYxaLpU+dFRt65Fn5JJpPFx8cErVuxaMkssViMEDI1NS8oyH/w4E5GRloNjd+OuB74y08PH97jl/AfP35w/0GEk6NrDe+rhqZGjBgrk8n27tshFAozMtL+OLh7yrSA5JR3dX+n1ta2BQX5l6+ck0gkT54+fP78KZermZubXfcW6gj2nK2Cqan55k27t29fF379MovF6tN7wLRp8xBCU6bMKS8vW7V6UUVFxbChvOXL1mZlfVy+4seVP6+v70v06N5r6JBRx08cDDtz0sHBedq0eXPnTSIOrjg7ux08cOpU8NE/Du4WCiscHVzWr9vJYrEQQp6duzk7ua1es2TihBmTJs6orvHFi1bt/X37ytWLEELa2joD+g8dOWJcDe+rBhrqGkcOh4aEHJ85e1x6eqq9veNPS1bb2drX/Z369uqTlpZ84q9Dv+7a1NHDc9nSX0JCTwSfPubq2qFTxy71+cxqAbdjaJb2L30/eqk1rT73k1M2iUSSmprcps2n+1InvUqcM3fioT+CFc+Az53c8H7Ghlp+g9CtBY0jPiFm+swxv+3ekp2d9fJl/G+/bXZ0dLGxsSW7rmYMurWgrlasXJDw2Ujyc/36DZk9a8HiRSvDr1+eMm2Umpq6RwfPWbMWfH4Oo2bBp4+dPn2sykUWltZ7d//5HYU3VxBOUFdLFq0SV1Z9fw8Om0NcuzOg/9CGNT5w4HAfn6pvMk+ntdL/pa30bYMG0NHRrcNaDaSupq6upq689psjGHMCgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIZ7NkaKGCEEZfSQH1ZWCuQqHW8huEcDZLUom8IFtIdhWggYrzxBUCKbWm74QjCGdzZeWkWpgtIrsK0EAFWSIbZ7VaV4NwNksdfLXevSjJeF1OdiGg3nLThbF3Cz371z5nL8yE0IwFb023bc/V1GVqG7HIrgXUgkJBhdmikoLKhIdF43+2qMsXXSGczduLf4tTXpbRaJSc9BY1BJVIpHR6bWOyZkXfTEUqkVm2U+3gV+1Eu1+BcAIcde3aNSIigpgErNWCMScAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAhy5uLiQXQL5IJwAR3FxcWSXQD4IJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYosjlcrJrAOCTESNGMJlMKpWalJRkbW3NYDCoVCqXy/3999/JLo0EdLILAOA/qampxA8UCiUlJQUhxGQyFyxYQHZd5IBuLcCIq6urTCb7/Blzc/NRo0aRVxGZIJwAI2PHjtXW1lY8ZLFYAQEBpFZEJggnwEivXr3Mzc0VD01NTYcOHUpqRWSCcAK8BAQEqKmpEbtNHo9HdjlkgnACvPTp08fS0lIul5uYmLTm3SYcrW29ykulZJdQrZHDxmVm/MYbORHnIlVUaVQl79rgPGfr8vaFIOZucX6miKkCnabvQ0FsVZp7T812nTSU9QoQztYj9i4/412FS3dtLQMm2bW0BEU54pePi7X06Z37atdh9XqDcLYWUf8UFeRUeg3UJ7uQlibqZj6DhboN0m30lqFv0yrw8yQ56SJIpjJ49NYV8KW5GaJGbxnC2SrkZFRQqBSyq2ix6HRqTrqw0ZuFcLYKgmKJvhmb7CpaLF1TFQFf0ujNwqmUVkFUIYNjC8ojEctEZbI6rFg/sOcEAFMQTgAwBeEEAFMQTgAwBeEEAFMQTgAwBeEEAFMQTgAwBeEEAFMQTgAwBeEEAFMQTtAMjAz44fCRVjfpO4QTNKa1QcuvhV8iu4oWAsIJGtPr1y/JLqHlgK+MgaoVFRVu2hyY+DLO3Mxy8OCRHz6k33/w7/GjZxFChYUF+/bvTEiMFQqFHTt2mTBumpmZBULIx9cDIbRt+7r9B369culODY2np6cePXYgJjZaLpc7OrrwRk1wdnZDCEml0jNnTx0/cRAh5NDOedLEmcTzCCE6nXH+QuiBP3YxmUwnJ7cVy4O4GlyEkEQiOfLnvsdPHuTmZjs5uQ0dPMrTsxuxyZBhfpMmzvzwIf3c+dOamlpdPLvPm7tk4+bVkZF3zcwsxo2Z0rt3f4SQQCA4c/bk02ePUlPf62jrenl5T5k8W0VFpUk+5prAnhNUbev2oPSM1G1b961ft/PJk8gnTyKpVCqRn4WLZ8bERi9c8POfh0O1NLXnzJ34MfMDQuj6tUiE0E9LVtecTLFYvGDRDBqNtmXznh3b9tNp9JWrFgqFQoTQwUN7Ll06E7R2+6qfN+jpGSxb8b/09E+3Nrp771ZZmWDL5j0/LQlMSIg5enQ/8fzuPVvPngseOiQg+NQV7x6+a9YuvXvvNrGIwWCEhB43N7e8Ef5w2tS54dcvL1w0w7dX339uPPbp6b9tx7pSQSlORd8DAAAgAElEQVRC6PyFkODTxwJGjd+4YdfMmfPv3P2H+OtAOggnqAKfX/z48YNRI8c7tHPS0dFdvGhVdnYmsSg+PiY9PfXnFes6d/LS1taZPWuBBlfz3LngujeekZFWVFQ4fNhoO1t7GxvbNYGb167dJpFI+CX8sDMnebyJHT08u3b1XrJ4lUcHz4LCfGIrDkd1/Lip7m4e3j18vby84+JfIIREItGNm1fHjJ40aOBwrga33w+DfXv1PfHXIcVr2baxHzRwOJPJ7OntjxBydHTx6elPp9N9evaWSCTpaSkIoVEjxx0+eLqnt5+7m0f3bj4+PXs/ffawsT/RhoBuLajC++S3CCEnJ1fioZqaWvv2ndIzUhFC8QkxDAajvXtHYhGFQnFz7RAb97zujZuammtqam3e+ou/Xz831w5OTq7ubh4IodjY5wghe3tHYjU6nR60dptiK2cnN8XPXA1NsUiEEHrzJkksFnf06KJY5ObaIfz6ZX4Jn+j0mptbEs+rqqoihCwtbYiHbDYHIVRaWkLsYJ9FPdq8Zc27928kEglCSEtLKVNd1heEE1SB+F+rqqqmeEZDg0v8IBCUVlZWEsNLBU1Nrbo3zmKxfvv10N/XLp49F3zkz33GxqaTJszw9+8nEJQihFRYVQ/26PT//q9SKBRFMQih/82f+tXKRYUFRDgVaxKoVU3SfvDQnmvXLs6cOb+jRxcDA8PDR37H5IAzhBNUgcVSQQhVisWKZ4qKC4kfdHR02Wz2hvW/fr4+jUqrV/vm5pazZy2YPGnW8+dPw69f3rg50MLSmvhbUF5eVvd2dHT1EEKLF600MTH7/Hl9fcM6tiCXy69cPTdi+JgB/T/dl4UIPA4gnKAKxNHXlNT3lpbWxPHM58+fGhgYIYRsbOwqKir09Q1NjE2JlTOzPmpy67HnTE9PTXwZ90PfQSoqKl5ePTp37tq3X9c3b5J6evvT6fTYuOft2jkRsVmxcoGPt3+fPgOqa8rUxJzFYiGEiI4xcZBZLpdzOJw6FlNZWVlRUaGr+2lGX7FY/PDRvbq/F6WCA0KgCibGphYWVsdPHPyY+UEgEOz6bZORkQmxqEP7Tp06eW3fvi4nJ5vPL7546cys2eOvX79M9Ff19PSjoh6/iIkiBm9VKinhb90WtP/Arg8fMzIy0k4FH5VIJE6Ormpqav5+/S5dOhN+/fKLmKg9e7dFRz8hglodDoczaeLME38dio+PEYvFd+/dXrJ0zq7fNtf9nTKZTHNzy/Drlz9mfuDzi7duD3J2cistLSkvL6/PB6YUsOcEVVu6JHD7zvXjJwy1sbb19++nqqqWlJRALNq0YdflK+eC1q94+TLezMzCz++HYcM+3Uhz7JgpR48dePrs4engq+pq6lW27OTkumjhz8eO/xF25iRCyKND5507DhC76Pk/Ltv12+YdOzdIpdI2NnZBv2xTHNGpDi9ggo2NXXDIsefPn6qqqjk6uCxevKpe73T1yo2/79sxafIIFRWVObMXubl5PH36cMKkYWfDrternUYH90ppFR79XSCXU52716PzyecXC4VCA4NPg7cVKxfQafR1QduVVmMz9jqKLygU9xyp17jNQrcWVG1t0PKFi2bcf/Avn1/818kj0dFPBg0aQXZRrQvsOVuFhuw5S/jbtgelp6fm5eVYmFuNHzeta1fvum8+cFDP6hYtW/ZLt67VLm2OlLTnhHC2Cg0I53fK+v8rir6lpamNw5WrjUhJ4YQDQkApjAyNyS6h2YMxJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgiuEWgUmmyqTwR9iZWEwaSqqjf/xwi+sVVDXpOdlVJBdRYuVm1Ghym38/RyEs1XQN2PDFxyURyqV6Zs1/qX8EM5WQVOPbmDGeng5l+xCWqBnN/LVuTQDc1ajtwxfGWtF4u7z099WOHfV1jZgIkodNgA1KswWv3pSpG3E7OivlO/iQThbl7cvBLH3ivMyRQwm1umUSKR0ev2m22x6qhp0N2/Ndp00lNQ+hLOVqiiTkl1CTfr163fhwgVi2ks8sTk0Zfc+4FRKK8VWxXq/JJYI2Ko0FgvrIpUNDggBgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACHLm4uJBdAvkgnABHcXFxZJdAPggnAJiCcAKAKQgnAJiCcAKAKQgnAJiCcAKAKQgnAJiCcAKAKQgnAJiCcAKAKQgnAJiCcAKAKQgnAJiCcAKAKQgnAJiCcAKAKYpcLie7BgA+ad++PfEDhUJBCMnlciqVOnPmzOnTp5NdGglgzwkwYmNjQ6VSqVQqhUKhUChUKtXS0nLcuHFk10UOCCfAyJgxY1gsluIhnU4fPHgwm80mtSjSQDgBRoYOHWpmZqZ4aGJiMnLkSFIrIhOEE+BlxIgRxM6TTqcPGTJERUWF7IpIA+EEeBkxYgSx8zQ1NR0+fDjZ5ZAJwgmwM3LkSCaTOXDgQA6HQ3YtZIJTKS1HdERRamIZjUbNSa8gu5bvJZFI6XQa2VV8L30LtkQss2yn2rG3VgM2h3C2EKc2p7f10NTUZ+gYqcCvFBMUKirMEvHzxXH3CyeusqTUs58K4WwJTm1O7+Cna2LbqjuBOCvIFEeEfJyy1qpeW0E4m71nN4toDJptew2yCwE1SYkXlPPFnv21674JHBBq9lISBVoGrDqsCMikbcR6F1dar00gnM0enUHVMYRw4o6ry1DVoEsq67EJhLPZy0qtkFPILgLUQW6GUC6rxygSwgkApiCcAGAKwgkApiCcAGAKwgkApiCcAGAKwgkApiCcAGAKwgkApiCcAGAKwgkApiCcAGAKwgkApiCcAAspKe95YwaQXQVeIJwAC6/fvCS7BOxAOFujy1fOjRs/ZNCQXhs3B+bkZPv4etyOuEEsSkyMW7ps3qDBPuMnDtu3/9eysjLi+bVBy4PWrXj48N6gIb38+3jOXzg9KSlB0eD1G1fmzJv0Q/9uc+ZNOnsuWDH3zeChvufOnZ6/cLqPr0dJaQlC6PyF0KXL5g0c1HP4yD5B61Z8zPyAEDp67MCWrWuJSs6cPYUQKiwsWL9hJW/MgCHD/DZsWp2RkVaX93U74sa48UN8fD3mzJuUlZ3p4+tx6/Z1hFBI6Ikf+ndTrEa8UGTk3ZqLX/PL0qB1K/44uNvH1+PY8YM+vh4JCbGKRt69e+Pj60HUryQQzlYn6VXir7s2eXv7/XX8fM8efkHrVyCEqFQqQujDx4wlS+cIRcK9e46uW7s9OfntwkUzJBIJMf964su4f25dO7D/r/C/H7CYrE1b1hAN3rp9fcvWtXa29sEnL0+bOvfsueC9+3YQixgMxtVrF9q0abtt6+8cNic+PmbP3m2Ojq5BQduXL1tbVFS4YeMqhNDkSbN4ARMMDAz/vR01csRYqVS6cPHMmNjohQt+/vNwqJam9py5E2uNQXp66oaNq3x9+166GDFl8uyNm1YTZde8Vc3FJ6e8S055t2HdziGDRxoYGN66Ha7Y8O69W1yuprGRyff9NmoC4Wx1bt68qq2tM3nSLC5X08urR0cPT8WiW7fCGXTGurXbzc0tLS2tlyxe/fbd6weRd4ilFeXlPy0JNDYyodPpvr36ZmSklZeXI4SuXbvo4uK+YP5yLS3t9u4dJ0+cdfFiWFFRIXEnPw0N7v/mLvHo0JlOpzs4OB89EjZ2zGR3N4+OHp6jRo5LSkrgl/C/qjA+PiY9PfXnFes6d/LS1taZPWuBBlfz3Lngmt/XjZtXNTW1JoyfrqGu4dGh88D+w+ryadRcfHZ25to1W728emhqag0cMDwi4oZUKiU2/PfOP316DyBuVagkEM5WJznlXbt2TopdSo/uvopFiYmx9vaOXK4m8dDQ0MjY2DQu/gXx0MzcUjEFu5qaOkKotLREJpMlJMZ29OiiaMTdvaNMJlNs1dbOQbGIRqNlZn5Y8fP8AYO8fXw9fl61ECFUXFT4VYXxCTEMBqO9e0fiIYVCcXPtEBv3vOb39e7d67ZtHWi0T1NROzq5Enf4rGGTWou3MLdS3Kylf78hgjLBkyeRCKHk5HcfP2b0+2FwzSV9p1p2+qDlEQhK9fUNFQ8VUSQWvXr90sfX4/P1iwoLiB+Iru9XxGJxZWXlkT/3Hflz3xdb/X/kmEym4snIyLurAhePHTN55oz5Nja2UdFPli6bV2WFlZWVX5WhqVnLpOnFxUUmJv/doYytUvuNA2sv/rP7EWpqanX18r4dcd3Lq8fde7fsbO0tLOo3D219QThbHRZLRVL53yRwBYX5ip+1dXSdnd0mT5r1+fpcDU1UPRUVFQ6H09u/f48evp8/b2xk+u3KV69dcHZ2mzZ1LvFQIKh6qkgdHV02m71h/a+fP0mj1nJ3BnV1DZFYpHhYXlFe3ZpSmbQBxRM7z7XrlpeUljyIvNPvhyE11/P9IJytjomJ2du3rxQPI/9/SIkQsrG2vfnP364u7RU7ydTUZFNT85obtLGxKxWUurt92tFVVlZmZX3U1zf4ds2SEr6hgZHi4f37EdU1WFFRoa9vaGL8KSSZWR81ubXsOQ0NjZ88jZTJZETxsbHRikUMBlMkEkkkEqIzn56W0oDiEUKdO3fV0OCGhp5IS0vx8+1bcz3fD8acrU5XL++0tJTg08fkcvmzqMfx8TGKRSNGjJXJZHv37RAKhRkZaX8c3D1lWkByyruaG5w+dV5k5J1r4ZdkMll8fEzQuhWLlswSi8XfrtnGxu5Z1OMXMVESiYQ4ZYIQys7JQgiZmpoXFOQ/eHAnIyOtQ/tOnTp5bd++Licnm88vvnjpzKzZ469fv1xzGd7efvn5efv2/yqRSB4/fhB25qRikYODs1wuv37jCnEeJTjkWAOKJ0a/P/QddO78aa8uPT4fDigJhLPV6dG919Aho46fODh0uP+Fi6HTps0jThsghDTUNY4cDmWrsGfOHjdh0vCY2Oiflqy2s7WvuUFnZ7eDB07Fxb0YOtx/ydI5ZWWC9et2fn73eIUpU+Z07uS1avWi3n275ORkL1+21r6tw/IVP966fd2zczdnJ7fVa5YQZ1w3bdjl7e0XtH7FkGF+5y+E+Pn9MGwYr+YyOnp4zpzx46NH9/z7eG7YuOrzznk7e8fZsxYcPLjbx9cjaP2KqZPnKI4V1b14gpeXt0gk6u3fvw6f9PeCe6U0e78veTduZZuqDtZUTSKRpKYmt2ljRzxMepU4Z+7EQ38EK55pGYqLi4YO9w9cvcmnp38jNhsSeuLy5bMn/7pY5eGxmgVvej9lrTWDVdezLzDmbHXiE2IWLZ41ZPDIgFETCgvzd+/Z6ujoYmNjS3ZduIuJic7M+nD8xMFf1mxtQDIbAMLZ6ri7eSxetDL8+uUp00apqal7dPCcNWuBUk+mN5bg08dOnz5W5SILS+u9u/9U6qsvXT6PRqNNnTKncycvpb6QAnRrm736dmubL5FIJK6s5lANoqipqTV5RfUD3VrQYrFYrBoO1bQ8reDvLQDNE4QTAExBOAHAFIQTAExBOAHAFIQTAExBOAHAFIQTAExBOJs5OdIzUWkO194BpGOsgupzPR6Es5mjILFIWpJf9UVtAB/lJZKSQjFDpR5/RyGczZ6ZnWppoYTsKkAtSgoqLR3qd/UvhLN5Kysr02ubf+dsFtmFgFrcPZvdpZ92vTaBcDZLAoEAIfTw4cP+/fvn5mVPWm0Zsi2lMBs6tzji51We3ZU6aoEZW62WOcq+Al8Za2aKiop++uknAwODDRs25OXl6enpEc+XlUgjL+e/jSm1cVEvKaisrRncSSVSGr1+/5W/n0goolAoFCqFgij/oTb8aJuGDj0loczKUbVLfx2uLqO+m0M4m4djx449f/589+7deXl5mZmZrq6uVa4mk6K8TJFc2ux/pzNmzNi7d+/nc942gVWrVqWnp9NoNDabzWKxmEwmjUaTSCRsNnvt2rUNaJBCoegaM2mMBsYbvs+Jrw8fPly7dm348OE6OjoCgWDu3LkIIT09PcXe8ltUGjIwawnfeLR11jO25tR6p5PGNXfx2GXLluUVFqIv59ONiopqyjIUYMyJnZcvX6ampiKE9u3bhxDS1NRECM2bN69t27Zkl9Z0duzY0cTJRAi5u7t369btqyc1NDSauAwF6NbiIicnx8DAYP/+/Y8ePdq4caOpadWTjrcSjx8/7tSpU9PMo/U5Pp8/fvz4zMxM4qFMJgsPDzcwqHqOaWWDPSf5Xrx44e/v/+TJE4TQmDFjTpw40cqTiRBavHhxZSUJh7W4XO7o0aMVNy8yMDCYOnXqn38qd+qw6kA4ySEQCIKCglatWkV0XMPCwgYNGkT85yC7NCx4enoq7hfWxEaPHm1jYyOTyVRVVcPDw69evSoUCkeMGPHyZVPfexu6tU0qIiIiOjr6p59+ysjIePHiRe/evRV/pAE+Xrx4sWjRon///VfxTGpqamBgoIuLy5IlS5qsDNhzKp1UKg0PDy8rK5NKpdevX/fw8EAImZmZDRo0CJJZncePH8tkMrJe3d3d/fNkIoQsLS2J4UavXr0ePHjQNGXAnlNZSktLKyoq9PX1J06caG5uHhgYSNyPBNRF165dIyIiMJwIs6SkJDAwkMPhrFu3Ttkdb9hzNjKhUIgQOnHixKBBg0pLSxFCx48fX7duHSSzXkgcc9ZMQ0Nj165dPXv27Nq16+XLtdz47DvBnrPRvHnzZuvWrb169RozZkxqaqqlpSXZFQHlCgoKyszMDAoK0tfXV0b7EM7vIhKJTp8+LRAI5s2bFxsbK5fL3dzcyC6qJSDrPGd9RUVFBQYGjho1atKkSY3eOO5vHk/v378PDg5GCKWkpAgEgsGDByOEXF1dIZmNhazznPXl4eFx7do1gUAwatSoV69e1WGLeoBw1kNiYqJIJKqsrFy5ciVxWy57e/t58+aZmZmRXVpL07VrVzzHnFWaN2/e5s2b169fv3PnzkZsFrq1tRMIBGpqajNnzhQKhYcOHWIwGM3ihnmg6QUHB//5559BQUFeXo1wm0AIZ02uXLmyZ8+eXbt2OTg4FBYWamvX75vsoMEiIyO7dOmC/5jzW3w+PzAwUE1NLSgo6Dt3/s3vzStbZmbmunXrwsLCEEKGhoYhISEODg4IIUhmU1q6dGmzGHN+i8vl/vbbbz169PDy8rpy5cr3NAXh/CQiIoII5Nu3b11dXYcMGYIQ6tixI2SSFM1rzPmtPn36PHny5Pnz57Nnz87Ly2tYI626W1tZWfnkyZNu3bpFR0eHhoaOHTu2uhkGAGiYZ8+eBQYG8ni8iRMn1nfb1rjnFAgEUqlUJBL16NEjOjoaIdShQ4etW7dCMvERGRlJ4rW1jahjx47h4eElJSUBAQGvX7+u17ataM8pkUjodPqaNWvu3r178+ZNGo3WrDtOLRu219Y22Pv379esWdOhQ4eFCxfWcZNWsed8+PDh9OnTExMTEUIjRoy4c+cOMXcT2XWBajX3Mee3bGxsTp48aWBg4Ofn9/Dhw7ps0mL3nCUlJefOnTM2Nu7Tp8+1a9eMjIzc3d3JLgoAVFxcHBgYqKGhERQUVPO5opa253z//n1ERARC6NatW+Xl5Z06dUII9evXD5LZvLSYMee3NDU1d+/e3a1bN09Pz6tXr9awZosKZ1xcnOLCumHDhs2dO1dLS4vsokBDHDlyRCJpyTeA6du379OnT58+fXr48OHq1mlR4bSzswsJCfHx8SG7EPC9dHV1m+PlQfU1f/780NDQ6pa2nPeflZW1Y8cOsqsAjWPr1q1NP28tKWr4G9RywlleXh4XF0d2FaBxtOAxZ921nHAaGxsTM02CFqD5XlvbiFpOONlstrOzM9lVgMbR8s5zNkDLCWdWVtaGDRvIrgI0jtYz5qxBywknjDlbEhhztqhwwpizJYExZ4sKJ4w5WxIYc7aocMKYsyWBMWeLCieMOVsSGHO2qHDCmLMlgTFniwonjDlbEhhztqhwwpizJYExZ4sKJ4w5WxIYc7aocMKYsyWBMWeLCieMOVsSGHMihJp9t37hwoX37t2Ty+UUCkUmk1GpVGJWpOfPn5NdGmi4rVu3kl0C+Zr9nnP69On6+vpUKpVCodBoNAqFIpfLYcag5g7GnC0hnA4ODk5OTp8/o6OjM2bMGPIqAo0AxpwtIZwIoQkTJujo6CgempmZ+fr6kloR+F4w5mwh4XR2dlYcClJVVR07dizZFYHvBec5W0g4EUKTJk0idp7W1tZ+fn5klwO+171792DM2ULC6eTk5OrqqqKiAqPNlmHFihUw5qzldgyVIvnzf4ty04VlpdImrKohxOLKosJCA0MDsguphaYug0KlmFiznbpqkF0Ldvr27ctkMhFCBQUFWlpaVCpVIpEYGxvXMPNys1ZQUDBmzJgbN25UubSmbn1uhuj87x/cvHXsPDRZqs1idG5CdgG1o1GphTnC0hLp6W3pAYvMqDQK2RVhhEajZWZmEj9nZ2cTN4oeP3482XWRo9pwfnwvfHytYOwKm6atp1XQ1GcghPRMWKE7M0b/ZE52ORhxdnbOzs4mbqhBsLW19fb2JrUo0lQ95pRJ0YNLeb5jjJu8nlbEwILt2EX7/sV8sgvBCI/HMzIyUjxszbvNasOZ8bacqUKj0aHHpVzGNpxXUSVkV4ERNzc3e3t7xUN7e/uuXbuSWhGZqg5ncY7YyIrT5MW0OiwOVc9EpbSwJd9Oq77GjBmjq6tL7DZ5PB7Z5ZCp6nAKy2WSytZ+lqlpCIor4aP+XPv27du1a0eMNrt37052OWRq7RdhgO8lR+UCaVmJpEIglVQ2zl3S+/WcnJ9OG9ArIDmhrFEapNMpbDUaR4POUadRms9YDcIJGqK0UJKaVPb2RZmwQiYormSq0NS0WGJRY50M1/3Ba1FJBnqcwW+U5pgqNEGRSFwhVdVgsNgUWzdVSwc1ri7u//lxrw/gprRIcu9CflGuhK7CVNPV0LZhk11Rnej+/w+CwopXL8pjH2RzdWg9hupq6TPILawGEE5QD3fPFbyJKdW31jZ1VSW7lgZS02arabMRQiW55ed/z7Sw5/iN1iO7qKq1kGtrgbJJJfLj69KK+HRbLzOuYXNN5uc09Dk2nqYCIevw6hRhOY7H5CCcoHbCMtkfy5MN2xloGqmRXUsj0zRUtWhvfHxdahkfuxNaEE5QizK+NOy3Dw6+lixVfIdn34OhQm/bw+L8vix+Pl7fg4Fwglqc2Jhq6tLyL+Q0czP+a2Ma2VV8AcIJanL5ULZVB+NW8tUZWy+z83szya7iPxBOUK2XT0rKBRQVdSbZhTQRliq9UkaPuVtMdiGfQDhBtR5cytez1ia7iialb6P98GoB2VV8AuEEVYu7z9e14NKYret/CIVKMbTVjrqFxc5T6R/9yIAfDh/5vVGaSk5+5+PrERf3olFaAzWLi+RztPD9ZtK5K1u37RmtjJbVdDjxD1tHOEFzJCiWVAikrWe0+Tkmhy6XUwqzxWQXAuEEVUl9WcY1aAmXATWMup5aSmI52VU03rW1Uqn0zNlTx08cRAg5tHOeNHGms7Pbp9egM85fCD3wxy4mk+nk5LZieRBXg4sQkkgkR/7c9/jJg9zcbCcnt6GDR3l6diM2KSkt+eOP366FX+JyNT06dJ4+7X8GBoZfveKJvw4Hnz76686D7ewdaygsNTV585Y1796/0dTUCly16dCRvZYW1osXrQwJPXH8xMHwvx8Qq+XkZPPGDFgftKNrV2+EUGJi3PETB1+9SuRqanXx7D5xwgxVVVWE0JpfltJoNAMDo5DQEyt/Xrdj54axY6aMGztF8SEMHe4/dMioyZNmNdYHS4rsNBFdRYm7zWfPrz56diEr552RQRs3Z7/uXXjEvEFrNvXp4zujrLz4ZsRhFpPd1tZz8A+LNDR0EUIiUfmps4HvkqOMDNp06ThMebUhhJgcRlYq+eFstD3nwUN7Ll06E7R2+6qfN+jpGSxb8b/09FRi0d17t8rKBFs27/lpSWBCQszRo/uJ53fv2Xr2XPDQIQHBp6549/Bds3bp3Xu3idAuX/FjfkHezh0H/jfvp9y8nOU//yiRfHF11a3b148eO7B65caakymVSpet+J+Wts7pU1e2bt4bEnYiIyONwajlSpcPHzOWLJ0jFAn37jm6bu325OS3CxfNIApgMBjJKe+SU95tWLfTo4OnT8/et26HKzZ8ERNVWlri26vvd3yQWBDwJQyWsuZbfB57I/TCOlPjtj8vuvCD/+x7D0MuXfuVWESjMe48OEmhUINW3Fz6Y1hKWuyNfw8Ri8IubsgvyJg5ae/E0Vuyc5NfvYlUUnkIIQaLVlZC/tV8jbPn5Jfww86cXDB/eUcPT4RQ585dy8vLCgrzzc0tEUIcjur4cVOJNSMf3o2Lf4EQEolEN25eHTN60qCBwxFC/X4YnJAQe+KvQ949fB8/eZCUlHD86FliczMzi7AzJwsL/zvAHRMTvWXrLzNn/Ejs5WoQFf0kNzdn88bdenr6enr68/+3jDdmQM1T9SKEbt0KZ9AZ69Zu53I1EUJLFq8ePXbgg8g7Pb39KBRKdnbmgX1/qaioIIT69xsSfv3y23evbdu0RQjdvXvLvq0DUXazVsaXamsp6xtLT6MvWVu4Dxu4FCGkrqbdx3dG2IX1vt6T1NW0EUK62qZ+3pMRQmy2ets2nh8+vkII8UvyYhNuBQxdbWHmhBAa0Gfey1f3lVQeQojOpJeXkh/Oxtlzpqa8RwjZ//9OjE6nB63d5u7mQTx0dnJTrMnV0BSLRAihN2+SxGJxR48uikVurh2Sk9/xS/jv37/lcDiK/+J2tvarfl6vr/9ptuj0jNRVgYt8e/XlBZy/ep8AABQXSURBVEyotbD379+oqKhYWX2a4NPAwFBf36DWcCYmxtrbOxLJRAgZGhoZG5sSf1MQQhbmVkQyEUKOji6mpua3boUjhORy+d17t/39+9ftM8Mak02jM5Sy55TJZCnpcXa2nRXP2Fp7yOWylNQY4qGpSTvFIjZbQygSIIQKiz4ihAz0rRSLzD5brdHRGFR2k1xITKFQrK2tq1vaOH8dBYJShJAKS6Xq1/jsjjSKKUmJTf43f+pXKxcVFpSVCVjVNIUQ+m33FolEoq2tU90KX7RWVMhmf3E+QEWl9i8HCwSlr16/9PH1+Kow4gcmi/X580MGjTwZ/OesmfNfxERVVJT7+f1Ql8IwJ5fJxMJKBrvx8ymRiKXSyuu3Dly/deDz50vLCv//xyouFSwr5yOEWMz/fpVMphK/5F0plFSKm+IWB3K5PDk5ubqljRNOVVU1hFB5eT1mfNHR1UMILV600sTE7PPn9fUNORzViopy4jbV327Yp/cAe3vHHTs3eHh4tnfvWPOrqKtriMWiz5+pqKh6oC+V/ffL0NbRdXZ2++qgDldDs8oN/Xv3P3Dwt6joJ48e3/fq0kNDvSXcZEFVgy5ptDlHvsBkqrCYnA5u/Vwce33+vI52TbP1q3K4CCFxpVDxjFDUONMLVUkilqpqkD8PQeN0a9u0aUun02PjPt3pXS6XL/95/o0bV2vYxNTEnMViIYTc3TyIf5YW1hbmVhwOx76tg1AofP0miVgzPT11waIZ79+/JR729u8/oP/QHt17bdi4il9SyxwzRobGZWVlikNTHzM/5OXlEj8zGEyRSKQ4zpSelqLYysbaNjc329WlvaI2LU3t6kaSGuoaPb397t69FRFxw9+vX90+MNzpmbBk0saZretbxkZ2FcLSNtYdiH+W5i7q6jqa3JpucqOlaYwQSk2PIx5KJJVv3z9VUnnEN8t1TVh1WFG5Giecampq/n79Ll06E3798ouYqD17t0VHP2nXzqmGTTgczqSJM0/8dSg+PkYsFt+9d3vJ0jm7ftuMEPLw8DQxMTt4cPf9B/8+i3q867fNebk5FhZWn2++9Kc1dDp985Y1NRfWpUsPJpO5bcc6oVD49t3rTZsD1dQ+fV3YwcFZLpdfv3GFOI8SHHJMsdWIEWNlMtnefTuEQmFGRtofB3dPmRaQnPKuulfp128IccxWcSqouTO0YJXmC5TUeD//2QlJd59EX5bJZClpMSfDVv5xdK5EUtNJf02uvqW5642Ig7l5aZWVolNnViNlzqJXmiswtmwp4UQIzf9xmZubx46dGxYtnhUfHxP0y7ZaD1ryAib8tCQwOOTYwME9f9u9xdjIdPHiVcQYdfvWfTK5LHDNT0uXzVNhszdt/O2rW6mqqqquWb35yZPI8xdCa3gJNTW1Det/FVZUDBjkPXPWuB7de+nq6hOL2tk7zp614ODB3T6+HkHrV0ydPIfY5xM7wyOHQ9kq7Jmzx02YNDwmNvqnJavtbO2rexV3Nw86ne7v16/F3O/V3J5Tml8hV87cHVYWbgtnn0hJjfllS98/jv2vQiiYPHYbg1FLGEYPX2Nu6rhr/4SV6304bI1O7Qeh2g7sNRg/t8zKifxrMKq+BeCT8MLKSuTq3QK/kTB56ihXl/YL5i9vxDZfv0maPWfCiWPnTE3rfVeiS/vS+k8x0jLA7kK5m6dyRVKOul7zmFyvEZUVCpFI0H9KU9xLsuZbAMLle9/l3bs3kZF3N25aPZo3sQHJxJlbd25hRhHZVZCgMKPItQcWR/WafTcs+PSx06ePVbnIwtJ67+4/lfrqBw/tfhb12N+/35TJs5X6Qk1P35ylY0AvyS3T0K+6gxf5+Ez47QNVLqqsFFXXTeUNC3Rq12i39Iu4dzzi/okqF7FZahWiqofNY0eua2fnVeUiQUGFqjrVtA0W/YVm360tLy+v7uwInU5XXEiALWy7tQih4jxJ+IkcI4evr2omiMQVImHV5zMqRGVsVtWRZnM0GPRGe7MiUblIVPVvXyIR06t5ITZbvbq/HTmvcn1HaTfZodqG39m6WeBwOBwOvl87bNY09ehOnuovo/MNbHW/XcpislnVXAmggapYXxlYLA6L1Wi//bzkgjaubBxOohBgzAlq4txVQ8+QWpiBxZePlar4Y6mGhrxDL4y6WhBOUIteo/SMTKgFaY1zTyE8FX4o0dSW9hnfFEdo6w7CCWrXpb+Wtq40710+2YUoRX5KgTqnsuewOl2t3ZQgnKBOfEbotfNg577OFuQr8aLWJiYoKM9OyrZxYPqP1Se7lio0+wNCoMk4ddEwa8OJvJqf/pyvZaapqs1uppNNy+VIkF9RnFnM1ab6j9bTMcLxUDmEE9QPV4/eb7JhboYoLpL/6m6uhq6Kqo4qhUqhs+gMFh1RlHU93XeiyCmVIkmlSCKTycsLy/g5FW1c1f1G6RpZV/vNRBxAOEG96Zux/Hj6fjz9jDcVOenC/ExRWaFEwqDy88ifsa5KXH2mVCxT5dJ1DBn63bQs7Gv6eho+IJyg4czs2GZ2WFxM0yJVHU4qHdHhWFGTYKvSMe0LArJVnUBVDTq/ANMuSguT+0HI1W6Z970E36nqcOoYscTCpphDpZUTFEuMrNg0RrM85gmUrepwGpizGEzKu5jSJq+ndXl0NdfNG6PrxQBWqh1Y9p1g+OG14PWzlnzRFrkiTmc5eXEtHeCqfVC1mo7WDpxh9G9Y7vndaRraDBU1Zc3/3WjkSCavesI+rHDU6Znvy5kqVCcvDTt38ufCANiq5VSKzyh9YZksP1NUxid/Auya5eTknDlzZt68eWQXUgsqDTl0VtM1YlFw/zMCSFb7eU4VVaqpbTM4l0V/n5tbEdPWQ53sQgBoHPDXGwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwFTLCSeTyaRSqWlpaWQXAkBdxcbGampWez8Oilzecu5A9+jRox07dhgaGvJ4vG7dupFdDgDVOnfuXEhIiL6+/qpVq4yMjKpcp0WFk/D48eOQkJDU1NRRo0bxeDz8b9AAWo+srKyQkJDQ0NDBgwfzeDwrK6saVm6B4SR8/PgxNDQ0JCRkxIgRAQEBFhYWZFcEWrWnT5+Ghoa+efOGx+MFBATQ6bXfbKHFhlMhLCwsNDTU2NiYx+N17dqV7HJAq3P+/PmQkBBdXV0ej9ejR4+6b9jyw0l49OhRSEhIWlpaQEAAj8ejUOB+tUC5srOzQ0JCQkJCBg8eHBAQYG1tXd8WWks4CR8/fiQ+r1GjRgUEBJibm5NdEWiBnj17FhIS8vr1ax6Px+Px6tKDrVLrCqdCWFhYSEiIqalpQEAA9HVBYzl//nxoaKi2tjaPx/P29v7O1lppOAkPHz4MDQ1NS0sj/sKRXQ5ornJycoge2cCBAwMCAmxsbBql2VYdTsKHDx+Io9vEqRczMzOyKwLNRlRUVEhIyKtXr4hjGQwGoxEbh3D+hzj1YmZmxuPxvLy8yC4HYO38+fNhYWGampo8Hq9nz57KeAkI59eIvm56ejpxPorscgBecnJyiD/iAwYMaMQebJUgnFXLyMgICQkJCwuDvi4gREdHh4SEvHz5kujBMplMZb8ihLMWxJ9Jc3PzgIAA6Ou2ThcuXAgNDeVyuTwez8fHp8leF8JZJw8fPgwJCfnw4QOPxxs1ahTZ5YCmkJubS/xp7tevH4/HU2oPtkoQznpIT08PDQ0NCwsjOjampqZkVwSUIjo6OjQ0NCEhgTjuwGKxSCkDwtkQxEktCwsLHo/XpUsXsssBjebixYshISFcLjcgIKBXr17kFgPhbLjIyMiQkJDMzMyAgADo6zZreXl5xLnuvn378ni8Nm3akF0RgnA2grS0tLCwsLCwMOIyIxMTE7IrAvUQHR0dFhYWFxdH/PrI6sFWCcLZOORyOfGn19LSMiAgAPq6+Lt06VJISIiGhgYOPdgqQTgb2YMHD0JDQ6Gvi638/HzikEGfPn14PJ6trS3ZFVULwqkUaWlpoaGhZ8+eJQ73QV8XB8+fPw8NDY2NjSV6sCoqKmRXVAsIpxLJZDKir2tlZcXj8Tw9Pb9awcfHZ9u2bR4eHiQV2Fpcvnw5JCRETU2Nx+Ph2YOtEoSzKTx48CAkJCQ7OzsgIGDkyJH/1969BzV15XEAP7l5kMQEkCBVE7SUJZItM20VQ5WdAibyUBZolARf7U5nde3Lot3Zimy6uxaqu+MUOh3c7RTt1na2QWsgTrvB8bHtFLbqVEWQNWMtCELAklCB5CaEJOwf1424BXmp9+Ty+/x178m5Ob9k8h1OLjfnUo05OTlWq1UqlR48eFAikdBdIwP19PRQP9xNT0/X6XRyuZzuiiYHwvnwtLW1GQwGo9FIXWaUk5PDYrH8fr9cLjcYDHRXxyjUDLaxsZG6XAT/GeyoIJwPm8/nq6qqKi8v9/v9gUalUrl///7A7vAwav6mr7d7yNnvpalMTHF5BH8WERXNly8Wjdqhpqbm8OHDIpFIp9OpVKqHXuD9BOGkR3Jy8uDgYGCXzWZnZ2fr9XqEUHfboOlvnTEJojkyAYcHC5HdhSCIgV4POeDtvk6u3Sbj8W8vSmyz2ajrYDMyMrRabdDNYEc1xaWHwDSRJMlmswO7Pp+vtrZWJpNlpq77xty7/o3HEKRyTEKEUG+3p7qic802WWPTxcAM9sSJE0E6gx0V/OWkQWpqqsfj4fP5HA6HIAiCILhcLpvN5rC5GYq92h0xkMyJ6L7uuvzvHvPFEgbMYEcF4aSH0WgUi8UCgSAkJEQkEgkEglmzZtlaQ9osruTcR+iuLmgcKWvVFkaLwpk5AWTmq8KfRqP5aeN35+yRMgEd5QSreTFCe9cgU8MJN/nBiKvfN+J7KJiAYeR2+ifQLyhBOAHAFIQTAExBOAHAFIQTAExBOAHAFIQTAExBOAHAFIQTAExBOAHAFIQTAExBOAHAFIQTAExBOAHAFIQTAExBOAHAFIQziLW0XEtTJZ45U7dWm/nrLesQQkXFhUXFhYEOx49/nqZKJEkSIZSnUZuOfXbo40rVSmV2Tsqfdu+0223jDkGSZLF+R9bqX6zMeLrGdKTyQMVzv1qDELpiaU5TJV6xNAd6btyUt/+vZdR2b6+9pLS4YH12nkZdukd/40Yb1X7UaFiTn1FX/6VqpfK9in2vbd/8uzdeGTmc/s3flpQW3793KLhBOIMYl8tFCB36pFKn3fT6jt+P27mq6hBBEDXVpz768GjT5Ya/f/T+uEO8U/52y/fflZd9UPXpFx0d7SdPmalB78Hn821//TcNl85vL9x1sLJqdnjESy8/32ntQAjxeDySdB479lnRzt3P5mpXZeaev3Cut9dOHeh2u8+crVu27JnJvAdMBuEMYiwWCyG0NPHp/LUbFPGPj9tfKo3euOEFsUgskUQuTVx29eqVe/d3OBxffXVSq920SK6IiJC8/NIODoc77qJTTU0N7e3XdxW9laRcHhEheXFrYWhY+NGj/6AKdrvdBQXPq1WZMtmCtLR0oVB4+l/HqQPr6r9ECCUpkyfzHjAZhDPoyeMUE+0pv9NTLA51Oh337t/e3ur1euP/F3sWi6VQJIwfzssNXC538VNLA0c9+cSSS40XAh3iF91+Qh6Pp1ZlnTxppna//vp08vIUkWj01aJnIGaujDSj8CZ8v1fqL+3EURNOoUAYaBm5PRaHY2BoaChNddfdmcLDZwe2eTxeYDt7tabGdKTT2iGJiDx7rl5f/PakKmQ2CCeT+fy+6RweFhaOEBr03FmZ3kk6x+rs9d2+c4REEikQCEpLykY+yiZGX7ksNjZOoUgwm01xcfECgTApCea0d0A4GYXH5d3q+zGwGzhNOjVz585HCFkszfK4eOqOhv9pbgzh8xFCIbwQhJDLRVI9HQ6HzdZDbcfGyl0uV1TUXOl8GdVi7eoMD5s91iirsnINVYc6OtrVqiwOBz6Qd8B3TkZRKBIsluaWlmsIoW/Pn6VOsUzZnDlRCQlPVB6o6Oi8YbP1lJXvGXD0Uw9FRy8Ui8T/NJuGh4e9Xu/ev/xBLA6lHlqyWKlULt+3762bN7v7+m7VmI5sfXFTbe2xsUZZkZZht/ecPVe/Kit3OtUyD4STUfJytaoVmVu2bkhTJZrNpo3rX0AITWdR/6Kdu+MX/XzzlnX5uiyn05HyjJpq53K5ev0ei6V5hXrpug2/TE1ZOW+eNDDQntLylBT17pKiPI3aWG1Qq7M0moKxhhAKhUuWJC2IfjQmJnbKdTIS3I4BI6c+/SFiPv9nT4bSXciYyt/de6nxwocHDt/H5/R4PPm6rC2bX129Km+yx9ZV33wsQbgoUXwf68EHTPEBbbq7uzqtN4zVhoULY2BO+1MQzhmtqalh14jL/f7PJx/XUCdsH5BTp2srD1TExz/+xzf/PNl/88wEMK3FCC3T2ntcYSuRRD7MSqYAprWAyfBP4IwFZ2sBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEyMCMdvlmNbaBTONy+EVhjL2KjcIJ0YipSEDPw7RXUUwcfR5JfMmuoRS0IFwYkT+lKirlXTc8tJdSHC4er5fGisQihn7GWbsCwtS+dui6003b/3gobsQ3F27OGBtcaoKougu5AGCn4xhhxzwfV5pZbFYUQsEBBt+5XgXNofVb/d43H6+kEjf+Ajd5TxYEE5MdbW6bV0e1wBMce/CIliiMHbkfP4cGW8C3YMbhBMATMF3TgAwBeEEAFMQTgAwBeEEAFMQTgAwBeEEAFP/BSqHYnk7/hoJAAAAAElFTkSuQmCC", "text/plain": [ "" ] @@ -684,7 +610,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "6c800a77-2359-4011-ab0c-dad22ed333b1", "metadata": {}, "outputs": [ @@ -694,135 +620,92 @@ "text": [ "================================\u001b[1m Human Message \u001b[0m=================================\n", "\n", - "Which sales agent made the most in sales in 2009?\n", + "Which genre on average has the longest tracks?\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", "Available tables: Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "Tool Calls:\n", - " sql_db_schema (call_rMs3sF1HVcAGggGf89ZNvtLE)\n", - " Call ID: call_rMs3sF1HVcAGggGf89ZNvtLE\n", + " sql_db_schema (call_qxKtYiHgf93AiTDin9ez5wFp)\n", + " Call ID: call_qxKtYiHgf93AiTDin9ez5wFp\n", " Args:\n", - " table_names: Employee,Invoice,Customer\n", + " table_names: Genre,Track\n", "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: sql_db_schema\n", "\n", "\n", - "CREATE TABLE \"Customer\" (\n", - "\t\"CustomerId\" INTEGER NOT NULL, \n", - "\t\"FirstName\" NVARCHAR(40) NOT NULL, \n", - "\t\"LastName\" NVARCHAR(20) NOT NULL, \n", - "\t\"Company\" NVARCHAR(80), \n", - "\t\"Address\" NVARCHAR(70), \n", - "\t\"City\" NVARCHAR(40), \n", - "\t\"State\" NVARCHAR(40), \n", - "\t\"Country\" NVARCHAR(40), \n", - "\t\"PostalCode\" NVARCHAR(10), \n", - "\t\"Phone\" NVARCHAR(24), \n", - "\t\"Fax\" NVARCHAR(24), \n", - "\t\"Email\" NVARCHAR(60) NOT NULL, \n", - "\t\"SupportRepId\" INTEGER, \n", - "\tPRIMARY KEY (\"CustomerId\"), \n", - "\tFOREIGN KEY(\"SupportRepId\") REFERENCES \"Employee\" (\"EmployeeId\")\n", + "CREATE TABLE \"Genre\" (\n", + "\t\"GenreId\" INTEGER NOT NULL, \n", + "\t\"Name\" NVARCHAR(120), \n", + "\tPRIMARY KEY (\"GenreId\")\n", ")\n", "\n", "/*\n", - "3 rows from Customer table:\n", - "CustomerId\tFirstName\tLastName\tCompany\tAddress\tCity\tState\tCountry\tPostalCode\tPhone\tFax\tEmail\tSupportRepId\n", - "1\tLuís\tGonçalves\tEmbraer - Empresa Brasileira de Aeronáutica S.A.\tAv. Brigadeiro Faria Lima, 2170\tSão José dos Campos\tSP\tBrazil\t12227-000\t+55 (12) 3923-5555\t+55 (12) 3923-5566\tluisg@embraer.com.br\t3\n", - "2\tLeonie\tKöhler\tNone\tTheodor-Heuss-Straße 34\tStuttgart\tNone\tGermany\t70174\t+49 0711 2842222\tNone\tleonekohler@surfeu.de\t5\n", - "3\tFrançois\tTremblay\tNone\t1498 rue Bélanger\tMontréal\tQC\tCanada\tH2G 1A7\t+1 (514) 721-4711\tNone\tftremblay@gmail.com\t3\n", + "3 rows from Genre table:\n", + "GenreId\tName\n", + "1\tRock\n", + "2\tJazz\n", + "3\tMetal\n", "*/\n", "\n", "\n", - "CREATE TABLE \"Employee\" (\n", - "\t\"EmployeeId\" INTEGER NOT NULL, \n", - "\t\"LastName\" NVARCHAR(20) NOT NULL, \n", - "\t\"FirstName\" NVARCHAR(20) NOT NULL, \n", - "\t\"Title\" NVARCHAR(30), \n", - "\t\"ReportsTo\" INTEGER, \n", - "\t\"BirthDate\" DATETIME, \n", - "\t\"HireDate\" DATETIME, \n", - "\t\"Address\" NVARCHAR(70), \n", - "\t\"City\" NVARCHAR(40), \n", - "\t\"State\" NVARCHAR(40), \n", - "\t\"Country\" NVARCHAR(40), \n", - "\t\"PostalCode\" NVARCHAR(10), \n", - "\t\"Phone\" NVARCHAR(24), \n", - "\t\"Fax\" NVARCHAR(24), \n", - "\t\"Email\" NVARCHAR(60), \n", - "\tPRIMARY KEY (\"EmployeeId\"), \n", - "\tFOREIGN KEY(\"ReportsTo\") REFERENCES \"Employee\" (\"EmployeeId\")\n", + "CREATE TABLE \"Track\" (\n", + "\t\"TrackId\" INTEGER NOT NULL, \n", + "\t\"Name\" NVARCHAR(200) NOT NULL, \n", + "\t\"AlbumId\" INTEGER, \n", + "\t\"MediaTypeId\" INTEGER NOT NULL, \n", + "\t\"GenreId\" INTEGER, \n", + "\t\"Composer\" NVARCHAR(220), \n", + "\t\"Milliseconds\" INTEGER NOT NULL, \n", + "\t\"Bytes\" INTEGER, \n", + "\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n", + "\tPRIMARY KEY (\"TrackId\"), \n", + "\tFOREIGN KEY(\"MediaTypeId\") REFERENCES \"MediaType\" (\"MediaTypeId\"), \n", + "\tFOREIGN KEY(\"GenreId\") REFERENCES \"Genre\" (\"GenreId\"), \n", + "\tFOREIGN KEY(\"AlbumId\") REFERENCES \"Album\" (\"AlbumId\")\n", ")\n", "\n", "/*\n", - "3 rows from Employee table:\n", - "EmployeeId\tLastName\tFirstName\tTitle\tReportsTo\tBirthDate\tHireDate\tAddress\tCity\tState\tCountry\tPostalCode\tPhone\tFax\tEmail\n", - "1\tAdams\tAndrew\tGeneral Manager\tNone\t1962-02-18 00:00:00\t2002-08-14 00:00:00\t11120 Jasper Ave NW\tEdmonton\tAB\tCanada\tT5K 2N1\t+1 (780) 428-9482\t+1 (780) 428-3457\tandrew@chinookcorp.com\n", - "2\tEdwards\tNancy\tSales Manager\t1\t1958-12-08 00:00:00\t2002-05-01 00:00:00\t825 8 Ave SW\tCalgary\tAB\tCanada\tT2P 2T3\t+1 (403) 262-3443\t+1 (403) 262-3322\tnancy@chinookcorp.com\n", - "3\tPeacock\tJane\tSales Support Agent\t2\t1973-08-29 00:00:00\t2002-04-01 00:00:00\t1111 6 Ave SW\tCalgary\tAB\tCanada\tT2P 5M5\t+1 (403) 262-3443\t+1 (403) 262-6712\tjane@chinookcorp.com\n", - "*/\n", - "\n", - "\n", - "CREATE TABLE \"Invoice\" (\n", - "\t\"InvoiceId\" INTEGER NOT NULL, \n", - "\t\"CustomerId\" INTEGER NOT NULL, \n", - "\t\"InvoiceDate\" DATETIME NOT NULL, \n", - "\t\"BillingAddress\" NVARCHAR(70), \n", - "\t\"BillingCity\" NVARCHAR(40), \n", - "\t\"BillingState\" NVARCHAR(40), \n", - "\t\"BillingCountry\" NVARCHAR(40), \n", - "\t\"BillingPostalCode\" NVARCHAR(10), \n", - "\t\"Total\" NUMERIC(10, 2) NOT NULL, \n", - "\tPRIMARY KEY (\"InvoiceId\"), \n", - "\tFOREIGN KEY(\"CustomerId\") REFERENCES \"Customer\" (\"CustomerId\")\n", - ")\n", - "\n", - "/*\n", - "3 rows from Invoice table:\n", - "InvoiceId\tCustomerId\tInvoiceDate\tBillingAddress\tBillingCity\tBillingState\tBillingCountry\tBillingPostalCode\tTotal\n", - "1\t2\t2009-01-01 00:00:00\tTheodor-Heuss-Straße 34\tStuttgart\tNone\tGermany\t70174\t1.98\n", - "2\t4\t2009-01-02 00:00:00\tUllevålsveien 14\tOslo\tNone\tNorway\t0171\t3.96\n", - "3\t8\t2009-01-03 00:00:00\tGrétrystraat 63\tBrussels\tNone\tBelgium\t1000\t5.94\n", + "3 rows from Track table:\n", + "TrackId\tName\tAlbumId\tMediaTypeId\tGenreId\tComposer\tMilliseconds\tBytes\tUnitPrice\n", + "1\tFor Those About To Rock (We Salute You)\t1\t1\t1\tAngus Young, Malcolm Young, Brian Johnson\t343719\t11170334\t0.99\n", + "2\tBalls to the Wall\t2\t2\t1\tNone\t342562\t5510424\t0.99\n", + "3\tFast As a Shark\t3\t2\t1\tF. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman\t230619\t3990994\t0.99\n", "*/\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "Tool Calls:\n", - " sql_db_query (call_TQyoJSV78xEWHqc69PiYHjdS)\n", - " Call ID: call_TQyoJSV78xEWHqc69PiYHjdS\n", + " sql_db_query (call_RPN3GABMfb6DTaFTLlwnZxVN)\n", + " Call ID: call_RPN3GABMfb6DTaFTLlwnZxVN\n", " Args:\n", - " query: SELECT e.FirstName, e.LastName, SUM(i.Total) as TotalSales\n", - "FROM Employee e\n", - "JOIN Customer c ON e.EmployeeId = c.SupportRepId\n", - "JOIN Invoice i ON c.CustomerId = i.CustomerId\n", - "WHERE strftime('%Y', i.InvoiceDate) = '2009'\n", - "GROUP BY e.EmployeeId\n", - "ORDER BY TotalSales DESC\n", + " query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgTrackLength\n", + "FROM Track\n", + "JOIN Genre ON Track.GenreId = Genre.GenreId\n", + "GROUP BY Genre.GenreId\n", + "ORDER BY AvgTrackLength DESC\n", "LIMIT 1;\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "Tool Calls:\n", - " sql_db_query (call_tSQOuxc7iQ1jvLzXSofCqK4V)\n", - " Call ID: call_tSQOuxc7iQ1jvLzXSofCqK4V\n", + " sql_db_query (call_PR4s8ymiF3ZQLaoZADXtdqcl)\n", + " Call ID: call_PR4s8ymiF3ZQLaoZADXtdqcl\n", " Args:\n", - " query: SELECT e.FirstName, e.LastName, SUM(i.Total) as TotalSales\n", - "FROM Employee e\n", - "JOIN Customer c ON e.EmployeeId = c.SupportRepId\n", - "JOIN Invoice i ON c.CustomerId = i.CustomerId\n", - "WHERE strftime('%Y', i.InvoiceDate) = '2009'\n", - "GROUP BY e.EmployeeId\n", - "ORDER BY TotalSales DESC\n", + " query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgTrackLength\n", + "FROM Track\n", + "JOIN Genre ON Track.GenreId = Genre.GenreId\n", + "GROUP BY Genre.GenreId\n", + "ORDER BY AvgTrackLength DESC\n", "LIMIT 1;\n", "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: sql_db_query\n", "\n", - "[('Steve', 'Johnson', 164.34)]\n", + "[('Sci Fi & Fantasy', 2911783.0384615385)]\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", - "The sales agent who made the most in sales in 2009 was Steve Johnson, with total sales of 164.34.\n" + "The genre with the longest tracks on average is \"Sci Fi & Fantasy,\" with an average track length of approximately 2,911,783 milliseconds.\n" ] } ], "source": [ - "question = \"Which sales agent made the most in sales in 2009?\"\n", + "question = \"Which genre on average has the longest tracks?\"\n", "\n", "for step in agent.stream(\n", " {\"messages\": [{\"role\": \"user\", \"content\": question}]},\n", @@ -838,7 +721,7 @@ "source": [ "!!! tip\n", "\n", - " See [LangSmith trace](https://smith.langchain.com/public/36a9ebbb-7cc4-4c54-b4db-cb12e124a649/r) for the above run." + " See [LangSmith trace](https://smith.langchain.com/public/94b8c9ac-12f7-4692-8706-836a1f30f1ea/r) for the above run." ] }, { From 5413f9db9f99f75ebb8a8e067ca13476fe8ebadd Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 7 Jul 2025 19:00:51 -0400 Subject: [PATCH 18/31] chore(deps): bump peter-evans/create-pull-request from 6 to 7 (#5365) Bumps [peter-evans/create-pull-request](https://github.com/peter-evans/create-pull-request) from 6 to 7. - [Release notes](https://github.com/peter-evans/create-pull-request/releases) - [Commits](https://github.com/peter-evans/create-pull-request/compare/v6...v7) --- updated-dependencies: - dependency-name: peter-evans/create-pull-request dependency-version: '7' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/uv_lock_ugprade.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/uv_lock_ugprade.yml b/.github/workflows/uv_lock_ugprade.yml index 8acd39393..de079ec4c 100644 --- a/.github/workflows/uv_lock_ugprade.yml +++ b/.github/workflows/uv_lock_ugprade.yml @@ -30,7 +30,7 @@ jobs: run: make lock-upgrade - name: Create Pull Request - uses: peter-evans/create-pull-request@v6 + uses: peter-evans/create-pull-request@v7 with: token: ${{ secrets.GITHUB_TOKEN }} commit-message: "chore: upgrade dependencies with `uv lock --upgrade`" From fa36a50444d05d6c47d79d3f96681b6d113c5b75 Mon Sep 17 00:00:00 2001 From: Fadel Akram Date: Tue, 8 Jul 2025 00:14:17 +0100 Subject: [PATCH 19/31] docs: correct link in docs/docs/how-tos/graph-api.md (#5377) Update graph-api.md --- docs/docs/how-tos/graph-api.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/docs/how-tos/graph-api.md b/docs/docs/how-tos/graph-api.md index 3cf08f8c5..4a71cc77d 100644 --- a/docs/docs/how-tos/graph-api.md +++ b/docs/docs/how-tos/graph-api.md @@ -163,7 +163,7 @@ Hello! In practice, there are additional considerations for updating lists of messages: - We may wish to update an existing message in the state. -- We may want to accept short-hands for [message formats](../concepts/low_level.md#using-messages-in-your-graph), such as [OpenAI format](https://python.langchain.com/docs/concepts/messages.md#openai-format). +- We may want to accept short-hands for [message formats](../concepts/low_level.md#using-messages-in-your-graph), such as [OpenAI format](https://python.langchain.com/docs/concepts/messages/#openai-format). LangGraph includes a built-in reducer `add_messages` that handles these considerations: @@ -1874,4 +1874,4 @@ except ImportError: print( "You likely need to install dependencies for pygraphviz, see more here https://github.com/pygraphviz/pygraphviz/blob/main/INSTALL.txt" ) -``` \ No newline at end of file +``` From cba4d9e3bcdc2595206200d655142f9e7f621a19 Mon Sep 17 00:00:00 2001 From: David <31293924+dreadn0ught@users.noreply.github.com> Date: Tue, 8 Jul 2025 01:18:55 +0200 Subject: [PATCH 20/31] docs: Update quick_start.md Rest API Guide (#5368) Update quick_start.md Rest API Guide The curl command in the quick start needs some minor changes to work out of the box. I hope by adding these changes then new users can get started more quickly --- docs/docs/cloud/quick_start.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/docs/cloud/quick_start.md b/docs/docs/cloud/quick_start.md index 2823b891d..41ad577e1 100644 --- a/docs/docs/cloud/quick_start.md +++ b/docs/docs/cloud/quick_start.md @@ -154,8 +154,9 @@ You can now test the API: ```bash curl -s --request POST \ - --url \ + --url /runs/stream \ --header 'Content-Type: application/json' \ + --header "X-Api-Key: \ --data "{ \"assistant_id\": \"agent\", \"input\": { From 4321ed0f870b795434e7f81f0b7cc10d3c925ca4 Mon Sep 17 00:00:00 2001 From: jito Date: Tue, 8 Jul 2025 08:19:16 +0900 Subject: [PATCH 21/31] Remove duplicate CONFIG_KEY_CHECKPOINT_MAP from RESERVED set (#5372) Signed-off-by: jitokim --- libs/langgraph/langgraph/constants.py | 1 - 1 file changed, 1 deletion(-) diff --git a/libs/langgraph/langgraph/constants.py b/libs/langgraph/langgraph/constants.py index ac53718e1..f524de39f 100644 --- a/libs/langgraph/langgraph/constants.py +++ b/libs/langgraph/langgraph/constants.py @@ -119,7 +119,6 @@ RESERVED = { CONFIG_KEY_STREAM, CONFIG_KEY_STREAM_WRITER, CONFIG_KEY_STORE, - CONFIG_KEY_CHECKPOINT_MAP, CONFIG_KEY_RESUMING, CONFIG_KEY_TASK_ID, CONFIG_KEY_CHECKPOINT_MAP, From 4c73b176ff28485d72aa819054e0a1c8d5db2cf4 Mon Sep 17 00:00:00 2001 From: Youssef Ahmed Mohamed Abdelrahman <109446360+unauthorised-401@users.noreply.github.com> Date: Tue, 8 Jul 2025 02:22:53 +0300 Subject: [PATCH 22/31] docs: fix typo in persistence (#5329) * docs: fix typo in application_structure * docs: fix typo in persistence --- docs/docs/concepts/persistence.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/docs/concepts/persistence.md b/docs/docs/concepts/persistence.md index 7c612cf60..f3d807946 100644 --- a/docs/docs/concepts/persistence.md +++ b/docs/docs/concepts/persistence.md @@ -5,7 +5,7 @@ search: # Persistence -LangGraph has a built-in persistence layer, implemented through checkpointers. When you compile graph with a checkpointer, the checkpointer saves a `checkpoint` of the graph state at every super-step. Those checkpoints are saved to a `thread`, which can be accessed after graph execution. Because `threads` allow access to graph's state after execution, several powerful capabilities including human-in-the-loop, memory, time travel, and fault-tolerance are all possible. Below, we'll discuss each of these concepts in more detail. +LangGraph has a built-in persistence layer, implemented through checkpointers. When you compile a graph with a checkpointer, the checkpointer saves a `checkpoint` of the graph state at every super-step. Those checkpoints are saved to a `thread`, which can be accessed after graph execution. Because `threads` allow access to graph's state after execution, several powerful capabilities including human-in-the-loop, memory, time travel, and fault-tolerance are all possible. Below, we'll discuss each of these concepts in more detail. ![Checkpoints](img/persistence/checkpoints.jpg) @@ -78,7 +78,7 @@ After we run the graph, we expect to see exactly 4 checkpoints: * checkpoint with the outputs of `node_a` `{'foo': 'a', 'bar': ['a']}` and `node_b` as the next node to be executed * checkpoint with the outputs of `node_b` `{'foo': 'b', 'bar': ['a', 'b']}` and no next nodes to be executed -Note that we `bar` channel values contain outputs from both nodes as we have a reducer for `bar` channel. +Note that the `bar` channel values contain outputs from both nodes as we have a reducer for `bar` channel. ### Get state From 87f2e6939542eb4ce61f009071d1d06a2e7dcf11 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Mon, 7 Jul 2025 23:24:32 +0000 Subject: [PATCH 23/31] chore[deps]: upgrade dependencies with `uv lock --upgrade` (#5358) * chore: upgrade dependencies with `uv lock --upgrade` * linting * upgrade PR title --------- Co-authored-by: sydney-runkle <54324534+sydney-runkle@users.noreply.github.com> Co-authored-by: Sydney Runkle --- .github/workflows/uv_lock_ugprade.yml | 4 +- .../langgraph/checkpoint/postgres/aio.py | 2 +- .../langgraph/checkpoint/postgres/shallow.py | 2 +- .../langgraph/store/postgres/base.py | 8 +- libs/checkpoint-postgres/uv.lock | 196 +-- .../langgraph/checkpoint/sqlite/aio.py | 2 +- libs/checkpoint-sqlite/uv.lock | 196 +-- libs/checkpoint/uv.lock | 317 ++--- libs/cli/examples/graphs/storm.py | 4 +- libs/cli/langgraph_cli/config.py | 5 +- libs/cli/langgraph_cli/templates.py | 2 +- libs/cli/tests/unit_tests/cli/test_cli.py | 12 +- .../tests/unit_tests/cli/test_templates.py | 18 +- libs/cli/uv.lock | 575 +++++---- libs/langgraph/tests/test_runnable.py | 4 +- libs/langgraph/uv.lock | 1077 +++++++++-------- libs/prebuilt/uv.lock | 190 +-- libs/sdk-py/uv.lock | 154 ++- 18 files changed, 1486 insertions(+), 1282 deletions(-) diff --git a/.github/workflows/uv_lock_ugprade.yml b/.github/workflows/uv_lock_ugprade.yml index de079ec4c..75d74890c 100644 --- a/.github/workflows/uv_lock_ugprade.yml +++ b/.github/workflows/uv_lock_ugprade.yml @@ -33,8 +33,8 @@ jobs: uses: peter-evans/create-pull-request@v7 with: token: ${{ secrets.GITHUB_TOKEN }} - commit-message: "chore: upgrade dependencies with `uv lock --upgrade`" - title: "chore: upgrade dependencies with `uv lock --upgrade`" + commit-message: "chore[deps]: upgrade dependencies with `uv lock --upgrade`" + title: "chore[deps]: upgrade dependencies with `uv lock --upgrade`" body: | This PR updates the dependencies in all Python packages using `uv lock --upgrade`. diff --git a/libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py b/libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py index 9fc7673b2..8775c2df5 100644 --- a/libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py +++ b/libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py @@ -452,7 +452,7 @@ class AsyncPostgresSaver(BasePostgresSaver): while True: try: yield asyncio.run_coroutine_threadsafe( - anext(aiter_), # noqa: F821 + anext(aiter_), # type: ignore[arg-type] # noqa: F821 self.loop, ).result() except StopAsyncIteration: diff --git a/libs/checkpoint-postgres/langgraph/checkpoint/postgres/shallow.py b/libs/checkpoint-postgres/langgraph/checkpoint/postgres/shallow.py index af2aacef8..16f4094ae 100644 --- a/libs/checkpoint-postgres/langgraph/checkpoint/postgres/shallow.py +++ b/libs/checkpoint-postgres/langgraph/checkpoint/postgres/shallow.py @@ -877,7 +877,7 @@ class AsyncShallowPostgresSaver(BasePostgresSaver): while True: try: yield asyncio.run_coroutine_threadsafe( - anext(aiter_), # noqa: F821 + anext(aiter_), # type: ignore[arg-type] # noqa: F821 self.loop, ).result() except StopAsyncIteration: diff --git a/libs/checkpoint-postgres/langgraph/store/postgres/base.py b/libs/checkpoint-postgres/langgraph/store/postgres/base.py index 6b57cebae..bf17c20cd 100644 --- a/libs/checkpoint-postgres/langgraph/store/postgres/base.py +++ b/libs/checkpoint-postgres/langgraph/store/postgres/base.py @@ -448,10 +448,8 @@ class BasePostgresStore(Generic[C]): score_operator, post_operator = get_distance_operator(self) post_operator = post_operator.replace("scored", "uniq") - vector_type = ( - cast(PostgresIndexConfig, self.index_config) - .get("ann_index_config", {}) - .get("vector_type", "vector") + vector_type = self.index_config.get("ann_index_config", {}).get( + "vector_type", "vector" ) # For hamming bit vectors, or “regular” vectors @@ -1141,7 +1139,7 @@ def _get_vector_type_ops(store: BasePostgresStore) -> str: if not store.index_config: return "vector_cosine_ops" - config = cast(PostgresIndexConfig, store.index_config) + config = store.index_config index_config = config.get("ann_index_config", _DEFAULT_ANN_CONFIG).copy() vector_type = cast(str, index_config.get("vector_type", "vector")) if vector_type not in ("vector", "halfvec"): diff --git 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f'Conversation with {interview_state["editor"].name}\n\n' + convo + return f"Conversation with {interview_state['editor'].name}\n\n" + convo async def refine_outline(state: ResearchState): diff --git a/libs/cli/langgraph_cli/config.py b/libs/cli/langgraph_cli/config.py index ece457aad..cd0356350 100644 --- a/libs/cli/langgraph_cli/config.py +++ b/libs/cli/langgraph_cli/config.py @@ -602,8 +602,7 @@ def validate_config(config: Config) -> Config: if not config["graphs"]: raise click.UsageError( - "No graphs found in config. 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Add at least one graph to 'graphs' dictionary." ) # Validate image_distro config @@ -1181,7 +1180,7 @@ def python_config_to_docker( ) for reqpath, destpath in local_deps.pip_reqs ) - pip_reqs_str += f'{os.linesep}RUN {pip_install} {" ".join("-r " + r for _,r in local_deps.pip_reqs)}' + pip_reqs_str += f"{os.linesep}RUN {pip_install} {' '.join('-r ' + r for _, r in local_deps.pip_reqs)}" pip_reqs_str = f"""# -- Installing local requirements -- {pip_reqs_str} # -- End of local requirements install --""" diff --git a/libs/cli/langgraph_cli/templates.py b/libs/cli/langgraph_cli/templates.py index cd336e255..4ea915228 100644 --- a/libs/cli/langgraph_cli/templates.py +++ b/libs/cli/langgraph_cli/templates.py @@ -123,7 +123,7 @@ def _download_repo_with_requests(repo_url: str, path: str) -> None: ) except error.HTTPError as e: click.secho( - f"❌ Error: Failed to download repository.\n" f"Details: {e}\n", + f"❌ Error: Failed to download repository.\nDetails: {e}\n", fg="red", bold=True, err=True, diff --git a/libs/cli/tests/unit_tests/cli/test_cli.py b/libs/cli/tests/unit_tests/cli/test_cli.py index d07d6f7a8..268f44e00 100644 --- a/libs/cli/tests/unit_tests/cli/test_cli.py +++ b/libs/cli/tests/unit_tests/cli/test_cli.py @@ -280,9 +280,9 @@ def test_version_option() -> None: assert result.exit_code == 0, "Expected exit code 0 for --version option" # Check that the output contains the correct version information - assert ( - "LangGraph CLI, version" in result.output - ), "Expected version information in output" + assert "LangGraph CLI, version" in result.output, ( + "Expected version information in output" + ) def test_dockerfile_command_basic() -> None: @@ -570,6 +570,6 @@ def test_build_generate_proper_build_context(): build_context_pattern = re.compile(r"--build-context\s+(\w+)=([^\s]+)") build_contexts = re.findall(build_context_pattern, result.output) - assert ( - len(build_contexts) == 2 - ), f"Expected 2 build contexts, but found {len(build_contexts)}" + assert len(build_contexts) == 2, ( + f"Expected 2 build contexts, but found {len(build_contexts)}" + ) diff --git a/libs/cli/tests/unit_tests/cli/test_templates.py b/libs/cli/tests/unit_tests/cli/test_templates.py index acd7651d0..cbdf4bbe1 100644 --- a/libs/cli/tests/unit_tests/cli/test_templates.py +++ b/libs/cli/tests/unit_tests/cli/test_templates.py @@ -42,18 +42,18 @@ def test_create_new_with_mocked_download(mock_urlopen: MagicMock) -> None: # Verify CLI command execution and success assert result.exit_code == 0, result.output - assert ( - "New project created" in result.output - ), "Expected success message in output." + assert "New project created" in result.output, ( + "Expected success message in output." + ) # Verify that the directory is not empty assert os.listdir(temp_dir), "Expected files to be created in temp directory." # Check for a known file in the extracted content extracted_files = [f.name for f in Path(temp_dir).glob("*")] - assert ( - "test-file.txt" in extracted_files 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langchain_anthropic import ChatAnthropic - from langmem.short_term import SummarizationNode + from langmem.short_term import SummarizationNode, RunningSummary from langchain_core.messages.utils import count_tokens_approximately from langgraph.prebuilt import create_react_agent from langgraph.prebuilt.chat_agent_executor import AgentState @@ -1372,7 +1372,7 @@ The problem with trimming or removing messages, as shown above, is that you may # NOTE: we're adding this key to keep track of previous summary information # to make sure we're not summarizing on every LLM call # highlight-next-line - context: dict[str, Any] # (2)! + context: dict[str, RunningSummary] # (2)! checkpointer = InMemorySaver() # (3)! @@ -1447,18 +1447,18 @@ The problem with trimming or removing messages, as shown above, is that you may from langgraph.graph import StateGraph, START, MessagesState from langgraph.checkpoint.memory import InMemorySaver # highlight-next-line - from langmem.short_term import SummarizationNode + from langmem.short_term import SummarizationNode, RunningSummary model = init_chat_model("anthropic:claude-3-7-sonnet-latest") summarization_model = model.bind(max_tokens=128) class State(MessagesState): # highlight-next-line - context: dict[str, Any] # (1)! + context: dict[str, RunningSummary] # (1)! class LLMInputState(TypedDict): # (2)! summarized_messages: list[AnyMessage] - context: dict[str, Any] + context: dict[str, RunningSummary] # highlight-next-line summarization_node = SummarizationNode( From 6f86a8c4cbbe04e98d980eae59e7c12bf89377ad Mon Sep 17 00:00:00 2001 From: lc-arjun Date: Tue, 8 Jul 2025 06:49:13 -0700 Subject: [PATCH 25/31] Revert "docs: add -tunnel flag for node server" (#5397) Revert "docs: add -tunnel flag for node server (#5373)" This reverts commit 4abf94846241f3546a0194efd40e109745e7d005. --- docs/docs/troubleshooting/studio.md | 4 +- libs/sdk-js/client.cjs | 1 - libs/sdk-js/client.d.cts | 1 - libs/sdk-js/client.d.ts | 1 - libs/sdk-js/client.js | 1 - libs/sdk-js/docs/README.md | 26 - libs/sdk-js/docs/auth/README.md | 18 - libs/sdk-js/docs/auth/classes/Auth.md | 75 --- .../sdk-js/docs/auth/classes/HTTPException.md | 185 ------- .../docs/auth/interfaces/AuthEventValueMap.md | 481 ------------------ .../docs/auth/type-aliases/AuthFilters.md | 15 - libs/sdk-js/docs/classes/AssistantsClient.md | 385 -------------- libs/sdk-js/docs/classes/Client.md | 98 ---- libs/sdk-js/docs/classes/CronsClient.md | 151 ------ libs/sdk-js/docs/classes/RunsClient.md | 469 ----------------- libs/sdk-js/docs/classes/StoreClient.md | 314 ------------ libs/sdk-js/docs/classes/ThreadsClient.md | 433 ---------------- libs/sdk-js/docs/functions/getApiKey.md | 32 -- libs/sdk-js/docs/interfaces/ClientConfig.md | 57 --- libs/sdk-js/docs/react/README.md | 18 - libs/sdk-js/docs/react/functions/useStream.md | 27 - .../sdk-js/docs/react/interfaces/UseStream.md | 227 --------- .../docs/react/interfaces/UseStreamOptions.md | 343 ------------- .../react/type-aliases/MessageMetadata.md | 42 -- libs/sdk-js/docs/type-aliases/RequestHook.md | 25 - libs/sdk-js/index.cjs | 1 - libs/sdk-js/index.d.cts | 1 - libs/sdk-js/index.d.ts | 1 - libs/sdk-js/index.js | 1 - libs/sdk-js/react.cjs | 1 - libs/sdk-js/react.d.cts | 1 - libs/sdk-js/react.d.ts | 1 - libs/sdk-js/react.js | 1 - 33 files changed, 2 insertions(+), 3435 deletions(-) delete mode 100644 libs/sdk-js/client.cjs delete mode 100644 libs/sdk-js/client.d.cts delete mode 100644 libs/sdk-js/client.d.ts delete mode 100644 libs/sdk-js/client.js delete mode 100644 libs/sdk-js/docs/README.md delete mode 100644 libs/sdk-js/docs/auth/README.md delete mode 100644 libs/sdk-js/docs/auth/classes/Auth.md delete mode 100644 libs/sdk-js/docs/auth/classes/HTTPException.md delete mode 100644 libs/sdk-js/docs/auth/interfaces/AuthEventValueMap.md delete mode 100644 libs/sdk-js/docs/auth/type-aliases/AuthFilters.md delete mode 100644 libs/sdk-js/docs/classes/AssistantsClient.md delete mode 100644 libs/sdk-js/docs/classes/Client.md delete mode 100644 libs/sdk-js/docs/classes/CronsClient.md delete mode 100644 libs/sdk-js/docs/classes/RunsClient.md delete mode 100644 libs/sdk-js/docs/classes/StoreClient.md delete mode 100644 libs/sdk-js/docs/classes/ThreadsClient.md delete mode 100644 libs/sdk-js/docs/functions/getApiKey.md delete mode 100644 libs/sdk-js/docs/interfaces/ClientConfig.md delete mode 100644 libs/sdk-js/docs/react/README.md delete mode 100644 libs/sdk-js/docs/react/functions/useStream.md delete mode 100644 libs/sdk-js/docs/react/interfaces/UseStream.md delete mode 100644 libs/sdk-js/docs/react/interfaces/UseStreamOptions.md delete mode 100644 libs/sdk-js/docs/react/type-aliases/MessageMetadata.md delete mode 100644 libs/sdk-js/docs/type-aliases/RequestHook.md delete mode 100644 libs/sdk-js/index.cjs delete mode 100644 libs/sdk-js/index.d.cts delete mode 100644 libs/sdk-js/index.d.ts delete mode 100644 libs/sdk-js/index.js delete mode 100644 libs/sdk-js/react.cjs delete mode 100644 libs/sdk-js/react.d.cts delete mode 100644 libs/sdk-js/react.d.ts delete mode 100644 libs/sdk-js/react.js diff --git a/docs/docs/troubleshooting/studio.md b/docs/docs/troubleshooting/studio.md index 3fcbdea7f..b06f325de 100644 --- a/docs/docs/troubleshooting/studio.md +++ b/docs/docs/troubleshooting/studio.md @@ -17,7 +17,7 @@ Safari blocks plain-HTTP traffic on localhost. When running Studio with `langgra ```shell # Requires @langchain/langgraph-cli>=0.0.26 - npx @langchain/langgraph-cli dev --tunnel + npx @langchain/langgraph-cli dev ``` The command outputs a URL in this format: @@ -55,7 +55,7 @@ Disable Brave Shields for LangSmith using the Brave icon in the URL bar. ```shell # Requires @langchain/langgraph-cli>=0.0.26 - npx @langchain/langgraph-cli dev --tunnel + npx @langchain/langgraph-cli dev ``` The command outputs a URL in this format: diff --git a/libs/sdk-js/client.cjs b/libs/sdk-js/client.cjs deleted file mode 100644 index 680e5bd38..000000000 --- a/libs/sdk-js/client.cjs +++ /dev/null @@ -1 +0,0 @@ -module.exports = require('./dist/client.cjs'); \ No newline at end of file diff --git a/libs/sdk-js/client.d.cts b/libs/sdk-js/client.d.cts deleted file mode 100644 index 1c03ff9cb..000000000 --- a/libs/sdk-js/client.d.cts +++ /dev/null @@ -1 +0,0 @@ -export * from './dist/client.js' \ No newline at end of file diff --git a/libs/sdk-js/client.d.ts b/libs/sdk-js/client.d.ts deleted file mode 100644 index 1c03ff9cb..000000000 --- a/libs/sdk-js/client.d.ts +++ /dev/null @@ -1 +0,0 @@ -export * from './dist/client.js' \ No newline at end of file diff --git a/libs/sdk-js/client.js b/libs/sdk-js/client.js deleted file mode 100644 index 1c03ff9cb..000000000 --- a/libs/sdk-js/client.js +++ /dev/null @@ -1 +0,0 @@ -export * from './dist/client.js' \ No newline at end of file diff --git a/libs/sdk-js/docs/README.md b/libs/sdk-js/docs/README.md deleted file mode 100644 index a1627b448..000000000 --- a/libs/sdk-js/docs/README.md +++ /dev/null @@ -1,26 +0,0 @@ -**@langchain/langgraph-sdk** - -*** - -# @langchain/langgraph-sdk - -## Classes - -- [AssistantsClient](classes/AssistantsClient.md) -- [Client](classes/Client.md) -- [CronsClient](classes/CronsClient.md) -- [RunsClient](classes/RunsClient.md) -- [StoreClient](classes/StoreClient.md) -- [ThreadsClient](classes/ThreadsClient.md) - -## Interfaces - -- [ClientConfig](interfaces/ClientConfig.md) - -## Type Aliases - -- [RequestHook](type-aliases/RequestHook.md) - -## Functions - -- [getApiKey](functions/getApiKey.md) diff --git a/libs/sdk-js/docs/auth/README.md b/libs/sdk-js/docs/auth/README.md deleted file mode 100644 index 351cb8490..000000000 --- a/libs/sdk-js/docs/auth/README.md +++ /dev/null @@ -1,18 +0,0 @@ -**@langchain/langgraph-sdk** - -*** - -# @langchain/langgraph-sdk/auth - -## Classes - -- [Auth](classes/Auth.md) -- [HTTPException](classes/HTTPException.md) - -## Interfaces - -- [AuthEventValueMap](interfaces/AuthEventValueMap.md) - -## Type Aliases - -- [AuthFilters](type-aliases/AuthFilters.md) diff --git a/libs/sdk-js/docs/auth/classes/Auth.md b/libs/sdk-js/docs/auth/classes/Auth.md deleted file mode 100644 index 4b4cc618a..000000000 --- a/libs/sdk-js/docs/auth/classes/Auth.md +++ /dev/null @@ -1,75 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / Auth - -# Class: Auth\ - -Defined in: [src/auth/index.ts:11](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/index.ts#L11) - -## Type Parameters - -• **TExtra** = \{\} - -• **TAuthReturn** *extends* `BaseAuthReturn` = `BaseAuthReturn` - -• **TUser** *extends* `BaseUser` = `ToUserLike`\<`TAuthReturn`\> - -## Constructors - -### new Auth() - -> **new Auth**\<`TExtra`, `TAuthReturn`, `TUser`\>(): [`Auth`](Auth.md)\<`TExtra`, `TAuthReturn`, `TUser`\> - -#### Returns - -[`Auth`](Auth.md)\<`TExtra`, `TAuthReturn`, `TUser`\> - -## Methods - -### authenticate() - -> **authenticate**\<`T`\>(`cb`): [`Auth`](Auth.md)\<`TExtra`, `T`\> - -Defined in: [src/auth/index.ts:25](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/index.ts#L25) - -#### Type Parameters - -• **T** *extends* `BaseAuthReturn` - -#### Parameters - -##### cb - -`AuthenticateCallback`\<`T`\> - -#### Returns - -[`Auth`](Auth.md)\<`TExtra`, `T`\> - -*** - -### on() - -> **on**\<`T`\>(`event`, `callback`): `this` - -Defined in: [src/auth/index.ts:32](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/index.ts#L32) - -#### Type Parameters - -• **T** *extends* `CallbackEvent` - -#### Parameters - -##### event - -`T` - -##### callback - -`OnCallback`\<`T`, `TUser`\> - -#### Returns - -`this` diff --git a/libs/sdk-js/docs/auth/classes/HTTPException.md b/libs/sdk-js/docs/auth/classes/HTTPException.md deleted file mode 100644 index 0db191af5..000000000 --- a/libs/sdk-js/docs/auth/classes/HTTPException.md +++ /dev/null @@ -1,185 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / HTTPException - -# Class: HTTPException - -Defined in: [src/auth/error.ts:66](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/error.ts#L66) - -## Extends - -- `Error` - -## Constructors - -### new HTTPException() - -> **new HTTPException**(`status`, `options`?): [`HTTPException`](HTTPException.md) - -Defined in: [src/auth/error.ts:70](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/error.ts#L70) - -#### Parameters - -##### status - -`number` - -##### options? - -###### cause? - -`unknown` - -###### headers? - -`HeadersInit` - -###### message? - -`string` - -#### Returns - -[`HTTPException`](HTTPException.md) - -#### Overrides - -`Error.constructor` - -## Properties - -### cause? - -> `optional` **cause**: `unknown` - -Defined in: node\_modules/typescript/lib/lib.es2022.error.d.ts:24 - -#### Inherited from - -`Error.cause` - -*** - -### headers - -> **headers**: `HeadersInit` - -Defined in: [src/auth/error.ts:68](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/error.ts#L68) - -*** - -### message - -> **message**: `string` - -Defined in: node\_modules/typescript/lib/lib.es5.d.ts:1077 - -#### Inherited from - -`Error.message` - -*** - -### name - -> **name**: `string` - -Defined in: node\_modules/typescript/lib/lib.es5.d.ts:1076 - -#### Inherited from - -`Error.name` - -*** - -### stack? - -> `optional` **stack**: `string` - -Defined in: node\_modules/typescript/lib/lib.es5.d.ts:1078 - -#### Inherited from - -`Error.stack` - -*** - -### status - -> **status**: `number` - -Defined in: [src/auth/error.ts:67](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/error.ts#L67) - -*** - -### prepareStackTrace()? - -> `static` `optional` **prepareStackTrace**: (`err`, `stackTraces`) => `any` - -Defined in: node\_modules/@types/node/globals.d.ts:28 - -Optional override for formatting stack traces - -#### Parameters - -##### err - -`Error` - -##### stackTraces - -`CallSite`[] - -#### Returns - -`any` - -#### See - -https://v8.dev/docs/stack-trace-api#customizing-stack-traces - -#### Inherited from - -`Error.prepareStackTrace` - -*** - -### stackTraceLimit - -> `static` **stackTraceLimit**: `number` - -Defined in: node\_modules/@types/node/globals.d.ts:30 - -#### Inherited from - -`Error.stackTraceLimit` - -## Methods - -### captureStackTrace() - -> `static` **captureStackTrace**(`targetObject`, `constructorOpt`?): `void` - -Defined in: node\_modules/@types/node/globals.d.ts:21 - -Create .stack property on a target object - -#### Parameters - -##### targetObject - -`object` - -##### constructorOpt? - -`Function` - -#### Returns - -`void` - -#### Inherited from - -`Error.captureStackTrace` diff --git a/libs/sdk-js/docs/auth/interfaces/AuthEventValueMap.md b/libs/sdk-js/docs/auth/interfaces/AuthEventValueMap.md deleted file mode 100644 index 686e1a39b..000000000 --- a/libs/sdk-js/docs/auth/interfaces/AuthEventValueMap.md +++ /dev/null @@ -1,481 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / AuthEventValueMap - -# Interface: AuthEventValueMap - -Defined in: [src/auth/types.ts:218](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L218) - -## Properties - -### assistants:create - -> **assistants:create**: `object` - -Defined in: [src/auth/types.ts:226](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L226) - -#### assistant\_id? - -> `optional` **assistant\_id**: `Maybe`\<`string`\> - -#### config? - -> `optional` **config**: `Maybe`\<`AssistantConfig`\> - -#### graph\_id - -> **graph\_id**: `string` - -#### if\_exists? - -> `optional` **if\_exists**: `Maybe`\<`"raise"` \| `"do_nothing"`\> - -#### metadata? - -> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> - -#### name? - -> `optional` **name**: `Maybe`\<`string`\> - -*** - -### assistants:delete - -> **assistants:delete**: `object` - -Defined in: [src/auth/types.ts:229](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L229) - -#### assistant\_id - -> **assistant\_id**: `string` - -*** - -### assistants:read - -> **assistants:read**: `object` - -Defined in: [src/auth/types.ts:227](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L227) - -#### assistant\_id - -> **assistant\_id**: `string` - -#### metadata? - -> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> - -*** - -### assistants:search - -> **assistants:search**: `object` - -Defined in: [src/auth/types.ts:230](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L230) - -#### graph\_id? - -> `optional` **graph\_id**: `Maybe`\<`string`\> - -#### limit? - -> `optional` **limit**: `Maybe`\<`number`\> - -#### metadata? - -> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> - -#### offset? - -> `optional` **offset**: `Maybe`\<`number`\> - -*** - -### assistants:update - -> **assistants:update**: `object` - -Defined in: [src/auth/types.ts:228](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L228) - -#### assistant\_id - -> **assistant\_id**: `string` - -#### config? - -> `optional` **config**: `Maybe`\<`AssistantConfig`\> - -#### graph\_id? - -> `optional` **graph\_id**: `Maybe`\<`string`\> - -#### metadata? - -> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> - -#### name? - -> `optional` **name**: `Maybe`\<`string`\> - -#### version? - -> `optional` **version**: `Maybe`\<`number`\> - -*** - -### crons:create - -> **crons:create**: `object` - -Defined in: [src/auth/types.ts:232](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L232) - -#### cron\_id? - -> `optional` **cron\_id**: `Maybe`\<`string`\> - -#### end\_time? - -> `optional` **end\_time**: `Maybe`\<`string`\> - -#### payload? - -> `optional` **payload**: `Maybe`\<`Record`\<`string`, `unknown`\>\> - -#### schedule - -> **schedule**: `string` - -#### thread\_id? - -> `optional` **thread\_id**: `Maybe`\<`string`\> - -#### user\_id? - -> `optional` **user\_id**: `Maybe`\<`string`\> - -*** - -### crons:delete - -> **crons:delete**: `object` - -Defined in: [src/auth/types.ts:235](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L235) - -#### cron\_id - -> **cron\_id**: `string` - -*** - -### crons:read - -> **crons:read**: `object` - -Defined in: [src/auth/types.ts:233](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L233) - -#### cron\_id - -> **cron\_id**: `string` - -*** - -### crons:search - -> **crons:search**: `object` - -Defined in: [src/auth/types.ts:236](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L236) - -#### assistant\_id? - -> `optional` **assistant\_id**: `Maybe`\<`string`\> - -#### limit? - -> `optional` **limit**: `Maybe`\<`number`\> - -#### offset? - -> `optional` **offset**: `Maybe`\<`number`\> - -#### thread\_id? - -> `optional` **thread\_id**: `Maybe`\<`string`\> - -*** - -### crons:update - -> **crons:update**: `object` - -Defined in: [src/auth/types.ts:234](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L234) - -#### cron\_id - -> **cron\_id**: `string` - -#### payload? - -> `optional` **payload**: `Maybe`\<`Record`\<`string`, `unknown`\>\> - -#### schedule? - -> `optional` **schedule**: `Maybe`\<`string`\> - -*** - -### store:delete - -> **store:delete**: `object` - -Defined in: [src/auth/types.ts:242](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L242) - -#### key - -> **key**: `string` - -#### namespace? - -> `optional` **namespace**: `Maybe`\<`string`[]\> - -*** - -### store:get - -> **store:get**: `object` - -Defined in: [src/auth/types.ts:239](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L239) - -#### key - -> **key**: `string` - -#### namespace - -> **namespace**: `Maybe`\<`string`[]\> - -*** - -### store:list\_namespaces - -> **store:list\_namespaces**: `object` - -Defined in: [src/auth/types.ts:241](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L241) - -#### limit? - -> `optional` **limit**: `Maybe`\<`number`\> - -#### max\_depth? - -> `optional` **max\_depth**: `Maybe`\<`number`\> - -#### namespace? - -> `optional` **namespace**: `Maybe`\<`string`[]\> - -#### offset? - -> `optional` **offset**: `Maybe`\<`number`\> - -#### suffix? - -> `optional` **suffix**: `Maybe`\<`string`[]\> - -*** - -### store:put - -> **store:put**: `object` - -Defined in: [src/auth/types.ts:238](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L238) - -#### key - -> **key**: `string` - -#### namespace - -> **namespace**: `string`[] - -#### value - -> **value**: `Record`\<`string`, `unknown`\> - -*** - -### store:search - -> **store:search**: `object` - -Defined in: [src/auth/types.ts:240](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L240) - -#### filter? - -> `optional` **filter**: `Maybe`\<`Record`\<`string`, `unknown`\>\> - -#### limit? - -> `optional` **limit**: `Maybe`\<`number`\> - -#### namespace? - -> `optional` **namespace**: `Maybe`\<`string`[]\> - -#### offset? - -> `optional` **offset**: `Maybe`\<`number`\> - -#### query? - -> `optional` **query**: `Maybe`\<`string`\> - -*** - -### threads:create - -> **threads:create**: `object` - -Defined in: [src/auth/types.ts:219](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L219) - -#### if\_exists? - -> `optional` **if\_exists**: `Maybe`\<`"raise"` \| `"do_nothing"`\> - -#### metadata? - -> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> - -#### thread\_id? - -> `optional` **thread\_id**: `Maybe`\<`string`\> - -*** - -### threads:create\_run - -> **threads:create\_run**: `object` - -Defined in: [src/auth/types.ts:224](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L224) - -#### after\_seconds? - -> `optional` **after\_seconds**: `Maybe`\<`number`\> - -#### assistant\_id - -> **assistant\_id**: `string` - -#### if\_not\_exists? - -> `optional` **if\_not\_exists**: `Maybe`\<`"reject"` \| `"create"`\> - -#### kwargs - -> **kwargs**: `Record`\<`string`, `unknown`\> - -#### metadata? - -> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> - -#### multitask\_strategy? - -> `optional` **multitask\_strategy**: `Maybe`\<`"reject"` \| `"interrupt"` \| `"rollback"` \| `"enqueue"`\> - -#### prevent\_insert\_if\_inflight? - -> `optional` **prevent\_insert\_if\_inflight**: `Maybe`\<`boolean`\> - -#### run\_id - -> **run\_id**: `string` - -#### status - -> **status**: `Maybe`\<`"pending"` \| `"running"` \| `"error"` \| `"success"` \| `"timeout"` \| `"interrupted"`\> - -#### thread\_id? - -> `optional` **thread\_id**: `Maybe`\<`string`\> - -*** - -### threads:delete - -> **threads:delete**: `object` - -Defined in: [src/auth/types.ts:222](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L222) - -#### run\_id? - -> `optional` **run\_id**: `Maybe`\<`string`\> - -#### thread\_id? - -> `optional` **thread\_id**: `Maybe`\<`string`\> - -*** - -### threads:read - -> **threads:read**: `object` - -Defined in: [src/auth/types.ts:220](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L220) - -#### thread\_id? - -> `optional` **thread\_id**: `Maybe`\<`string`\> - -*** - -### threads:search - -> **threads:search**: `object` - -Defined in: [src/auth/types.ts:223](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L223) - -#### limit? - -> `optional` **limit**: `Maybe`\<`number`\> - -#### metadata? - -> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> - -#### offset? - -> `optional` **offset**: `Maybe`\<`number`\> - -#### status? - -> `optional` **status**: `Maybe`\<`"error"` \| `"interrupted"` \| `"idle"` \| `"busy"` \| `string` & `object`\> - -#### thread\_id? - -> `optional` **thread\_id**: `Maybe`\<`string`\> - -#### values? - -> `optional` **values**: `Maybe`\<`Record`\<`string`, `unknown`\>\> - -*** - -### threads:update - -> **threads:update**: `object` - -Defined in: [src/auth/types.ts:221](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L221) - -#### action? - -> `optional` **action**: `Maybe`\<`"interrupt"` \| `"rollback"`\> - -#### metadata? - -> `optional` **metadata**: `Maybe`\<`Record`\<`string`, `unknown`\>\> - -#### thread\_id? - -> `optional` **thread\_id**: `Maybe`\<`string`\> diff --git a/libs/sdk-js/docs/auth/type-aliases/AuthFilters.md b/libs/sdk-js/docs/auth/type-aliases/AuthFilters.md deleted file mode 100644 index b9b2e1fb2..000000000 --- a/libs/sdk-js/docs/auth/type-aliases/AuthFilters.md +++ /dev/null @@ -1,15 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / AuthFilters - -# Type Alias: AuthFilters\ - -> **AuthFilters**\<`TKey`\>: \{ \[key in TKey\]: string \| \{ \[op in "$contains" \| "$eq"\]?: string \} \} - -Defined in: [src/auth/types.ts:367](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/auth/types.ts#L367) - -## Type Parameters - -• **TKey** *extends* `string` \| `number` \| `symbol` diff --git a/libs/sdk-js/docs/classes/AssistantsClient.md b/libs/sdk-js/docs/classes/AssistantsClient.md deleted file mode 100644 index 571dc16e8..000000000 --- a/libs/sdk-js/docs/classes/AssistantsClient.md +++ /dev/null @@ -1,385 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / AssistantsClient - -# Class: AssistantsClient - -Defined in: [client.ts:431](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L431) - -## Extends - -- `BaseClient` - -## Constructors - -### new AssistantsClient() - -> **new AssistantsClient**(`config`?): [`AssistantsClient`](AssistantsClient.md) - -Defined in: [client.ts:183](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L183) - -#### Parameters - -##### config? - -[`ClientConfig`](../interfaces/ClientConfig.md) - -#### Returns - -[`AssistantsClient`](AssistantsClient.md) - -#### Inherited from - -`BaseClient.constructor` - -## Methods - -### create() - -> **create**(`payload`): `Promise`\<`Assistant`\> - -Defined in: [client.ts:496](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L496) - -Create a new assistant. - -#### Parameters - -##### payload - -Payload for creating an assistant. - -###### assistantId? - -`string` - -###### config? - -`Config` - -###### description? - -`string` - -###### graphId - -`string` - -###### ifExists? - -`OnConflictBehavior` - -###### metadata? - -`Metadata` - -###### name? - -`string` - -#### Returns - -`Promise`\<`Assistant`\> - -The created assistant. - -*** - -### delete() - -> **delete**(`assistantId`): `Promise`\<`void`\> - -Defined in: [client.ts:552](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L552) - -Delete an assistant. - -#### Parameters - -##### assistantId - -`string` - -ID of the assistant. - -#### Returns - -`Promise`\<`void`\> - -*** - -### get() - -> **get**(`assistantId`): `Promise`\<`Assistant`\> - -Defined in: [client.ts:438](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L438) - -Get an assistant by ID. - -#### Parameters - -##### assistantId - -`string` - -The ID of the assistant. - -#### Returns - -`Promise`\<`Assistant`\> - -Assistant - -*** - -### getGraph() - -> **getGraph**(`assistantId`, `options`?): `Promise`\<`AssistantGraph`\> - -Defined in: [client.ts:448](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L448) - -Get the JSON representation of the graph assigned to a runnable - -#### Parameters - -##### assistantId - -`string` - -The ID of the assistant. - -##### options? - -###### xray? - -`number` \| `boolean` - -Whether to include subgraphs in the serialized graph representation. If an integer value is provided, only subgraphs with a depth less than or equal to the value will be included. - -#### Returns - -`Promise`\<`AssistantGraph`\> - -Serialized graph - -*** - -### getSchemas() - -> **getSchemas**(`assistantId`): `Promise`\<`GraphSchema`\> - -Defined in: [client.ts:462](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L462) - -Get the state and config schema of the graph assigned to a runnable - -#### Parameters - -##### assistantId - -`string` - -The ID of the assistant. - -#### Returns - -`Promise`\<`GraphSchema`\> - -Graph schema - -*** - -### getSubgraphs() - -> **getSubgraphs**(`assistantId`, `options`?): `Promise`\<`Subgraphs`\> - -Defined in: [client.ts:473](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L473) - -Get the schemas of an assistant by ID. - -#### Parameters - -##### assistantId - -`string` - -The ID of the assistant to get the schema of. - -##### options? - -Additional options for getting subgraphs, such as namespace or recursion extraction. - -###### namespace? - -`string` - -###### recurse? - -`boolean` - -#### Returns - -`Promise`\<`Subgraphs`\> - -The subgraphs of the assistant. - -*** - -### getVersions() - -> **getVersions**(`assistantId`, `payload`?): `Promise`\<`AssistantVersion`[]\> - -Defined in: [client.ts:590](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L590) - -List all versions of an assistant. - -#### Parameters - -##### assistantId - -`string` - -ID of the assistant. - -##### payload? - -###### limit? - -`number` - -###### metadata? - -`Metadata` - -###### offset? - -`number` - -#### Returns - -`Promise`\<`AssistantVersion`[]\> - -List of assistant versions. - -*** - -### search() - -> **search**(`query`?): `Promise`\<`Assistant`[]\> - -Defined in: [client.ts:563](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L563) - -List assistants. - -#### Parameters - -##### query? - -Query options. - -###### graphId? - -`string` - -###### limit? - -`number` - -###### metadata? - -`Metadata` - -###### offset? - -`number` - -###### sortBy? - -`AssistantSortBy` - -###### sortOrder? - -`SortOrder` - -#### Returns - -`Promise`\<`Assistant`[]\> - -List of assistants. - -*** - -### setLatest() - -> **setLatest**(`assistantId`, `version`): `Promise`\<`Assistant`\> - -Defined in: [client.ts:618](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L618) - -Change the version of an assistant. - -#### Parameters - -##### assistantId - -`string` - -ID of the assistant. - -##### version - -`number` - -The version to change to. - -#### Returns - -`Promise`\<`Assistant`\> - -The updated assistant. - -*** - -### update() - -> **update**(`assistantId`, `payload`): `Promise`\<`Assistant`\> - -Defined in: [client.ts:525](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L525) - -Update an assistant. - -#### Parameters - -##### assistantId - -`string` - -ID of the assistant. - -##### payload - -Payload for updating the assistant. - -###### config? - -`Config` - -###### description? - -`string` - -###### graphId? - -`string` - -###### metadata? - -`Metadata` - -###### name? - -`string` - -#### Returns - -`Promise`\<`Assistant`\> - -The updated assistant. diff --git a/libs/sdk-js/docs/classes/Client.md b/libs/sdk-js/docs/classes/Client.md deleted file mode 100644 index b0afc23de..000000000 --- a/libs/sdk-js/docs/classes/Client.md +++ /dev/null @@ -1,98 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / Client - -# Class: Client\ - -Defined in: [client.ts:1620](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1620) - -## Type Parameters - -• **TStateType** = `DefaultValues` - -• **TUpdateType** = `TStateType` - -• **TCustomEventType** = `unknown` - -## Constructors - -### new Client() - -> **new Client**\<`TStateType`, `TUpdateType`, `TCustomEventType`\>(`config`?): [`Client`](Client.md)\<`TStateType`, `TUpdateType`, `TCustomEventType`\> - -Defined in: [client.ts:1656](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1656) - -#### Parameters - -##### config? - -[`ClientConfig`](../interfaces/ClientConfig.md) - -#### Returns - -[`Client`](Client.md)\<`TStateType`, `TUpdateType`, `TCustomEventType`\> - -## Properties - -### ~ui - -> **~ui**: `UiClient` - -Defined in: [client.ts:1654](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1654) - -**`Internal`** - -The client for interacting with the UI. - Used by LoadExternalComponent and the API might change in the future. - -*** - -### assistants - -> **assistants**: [`AssistantsClient`](AssistantsClient.md) - -Defined in: [client.ts:1628](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1628) - -The client for interacting with assistants. - -*** - -### crons - -> **crons**: [`CronsClient`](CronsClient.md) - -Defined in: [client.ts:1643](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1643) - -The client for interacting with cron runs. - -*** - -### runs - -> **runs**: [`RunsClient`](RunsClient.md)\<`TStateType`, `TUpdateType`, `TCustomEventType`\> - -Defined in: [client.ts:1638](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1638) - -The client for interacting with runs. - -*** - -### store - -> **store**: [`StoreClient`](StoreClient.md) - -Defined in: [client.ts:1648](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1648) - -The client for interacting with the KV store. - -*** - -### threads - -> **threads**: [`ThreadsClient`](ThreadsClient.md)\<`TStateType`, `TUpdateType`\> - -Defined in: [client.ts:1633](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1633) - -The client for interacting with threads. diff --git a/libs/sdk-js/docs/classes/CronsClient.md b/libs/sdk-js/docs/classes/CronsClient.md deleted file mode 100644 index ae0017d9c..000000000 --- a/libs/sdk-js/docs/classes/CronsClient.md +++ /dev/null @@ -1,151 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / CronsClient - -# Class: CronsClient - -Defined in: [client.ts:334](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L334) - -## Extends - -- `BaseClient` - -## Constructors - -### new CronsClient() - -> **new CronsClient**(`config`?): [`CronsClient`](CronsClient.md) - -Defined in: [client.ts:183](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L183) - -#### Parameters - -##### config? - -[`ClientConfig`](../interfaces/ClientConfig.md) - -#### Returns - -[`CronsClient`](CronsClient.md) - -#### Inherited from - -`BaseClient.constructor` - -## Methods - -### create() - -> **create**(`assistantId`, `payload`?): `Promise`\<`CronCreateResponse`\> - -Defined in: [client.ts:375](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L375) - -#### Parameters - -##### assistantId - -`string` - -Assistant ID to use for this cron job. - -##### payload? - -`CronsCreatePayload` - -Payload for creating a cron job. - -#### Returns - -`Promise`\<`CronCreateResponse`\> - -*** - -### createForThread() - -> **createForThread**(`threadId`, `assistantId`, `payload`?): `Promise`\<`CronCreateForThreadResponse`\> - -Defined in: [client.ts:342](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L342) - -#### Parameters - -##### threadId - -`string` - -The ID of the thread. - -##### assistantId - -`string` - -Assistant ID to use for this cron job. - -##### payload? - -`CronsCreatePayload` - -Payload for creating a cron job. - -#### Returns - -`Promise`\<`CronCreateForThreadResponse`\> - -The created background run. - -*** - -### delete() - -> **delete**(`cronId`): `Promise`\<`void`\> - -Defined in: [client.ts:402](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L402) - -#### Parameters - -##### cronId - -`string` - -Cron ID of Cron job to delete. - -#### Returns - -`Promise`\<`void`\> - -*** - -### search() - -> **search**(`query`?): `Promise`\<`Cron`[]\> - -Defined in: [client.ts:413](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L413) - -#### Parameters - -##### query? - -Query options. - -###### assistantId? - -`string` - -###### limit? - -`number` - -###### offset? - -`number` - -###### threadId? - -`string` - -#### Returns - -`Promise`\<`Cron`[]\> - -List of crons. diff --git a/libs/sdk-js/docs/classes/RunsClient.md b/libs/sdk-js/docs/classes/RunsClient.md deleted file mode 100644 index 15f666453..000000000 --- a/libs/sdk-js/docs/classes/RunsClient.md +++ /dev/null @@ -1,469 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / RunsClient - -# Class: RunsClient\ - -Defined in: [client.ts:912](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L912) - -## Extends - -- `BaseClient` - -## Type Parameters - -• **TStateType** = `DefaultValues` - -• **TUpdateType** = `TStateType` - -• **TCustomEventType** = `unknown` - -## Constructors - -### new RunsClient() - -> **new RunsClient**\<`TStateType`, `TUpdateType`, `TCustomEventType`\>(`config`?): [`RunsClient`](RunsClient.md)\<`TStateType`, `TUpdateType`, `TCustomEventType`\> - -Defined in: [client.ts:183](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L183) - -#### Parameters - -##### config? - -[`ClientConfig`](../interfaces/ClientConfig.md) - -#### Returns - -[`RunsClient`](RunsClient.md)\<`TStateType`, `TUpdateType`, `TCustomEventType`\> - -#### Inherited from - -`BaseClient.constructor` - -## Methods - -### cancel() - -> **cancel**(`threadId`, `runId`, `wait`, `action`): `Promise`\<`void`\> - -Defined in: [client.ts:1227](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1227) - -Cancel a run. - -#### Parameters - -##### threadId - -`string` - -The ID of the thread. - -##### runId - -`string` - -The ID of the run. - -##### wait - -`boolean` = `false` - -Whether to block when canceling - -##### action - -`CancelAction` = `"interrupt"` - -Action to take when cancelling the run. Possible values are `interrupt` or `rollback`. Default is `interrupt`. - -#### Returns - -`Promise`\<`void`\> - -*** - -### create() - -> **create**(`threadId`, `assistantId`, `payload`?): `Promise`\<`Run`\> - -Defined in: [client.ts:1026](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1026) - -Create a run. - -#### Parameters - -##### threadId - -`string` - -The ID of the thread. - -##### assistantId - -`string` - -Assistant ID to use for this run. - -##### payload? - -`RunsCreatePayload` - -Payload for creating a run. - -#### Returns - -`Promise`\<`Run`\> - -The created run. - -*** - -### createBatch() - -> **createBatch**(`payloads`): `Promise`\<`Run`[]\> - -Defined in: [client.ts:1076](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1076) - -Create a batch of stateless background runs. - -#### Parameters - -##### payloads - -`RunsCreatePayload` & `object`[] - -An array of payloads for creating runs. - -#### Returns - -`Promise`\<`Run`[]\> - -An array of created runs. - -*** - -### delete() - -> **delete**(`threadId`, `runId`): `Promise`\<`void`\> - -Defined in: [client.ts:1329](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1329) - -Delete a run. - -#### Parameters - -##### threadId - -`string` - -The ID of the thread. - -##### runId - -`string` - -The ID of the run. - -#### Returns - -`Promise`\<`void`\> - -*** - -### get() - -> **get**(`threadId`, `runId`): `Promise`\<`Run`\> - -Defined in: [client.ts:1214](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1214) - -Get a run by ID. - -#### Parameters - -##### threadId - -`string` - -The ID of the thread. - -##### runId - -`string` - -The ID of the run. - -#### Returns - -`Promise`\<`Run`\> - -The run. - -*** - -### join() - -> **join**(`threadId`, `runId`, `options`?): `Promise`\<`void`\> - -Defined in: [client.ts:1249](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1249) - -Block until a run is done. - -#### Parameters - -##### threadId - -`string` - -The ID of the thread. - -##### runId - -`string` - -The ID of the run. - -##### options? - -###### signal? - -`AbortSignal` - -#### Returns - -`Promise`\<`void`\> - -*** - -### joinStream() - -> **joinStream**(`threadId`, `runId`, `options`?): `AsyncGenerator`\<\{ `data`: `any`; `event`: `StreamEvent`; `id`: `string`; \}\> - -Defined in: [client.ts:1274](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1274) - -Stream output from a run in real-time, until the run is done. - -#### Parameters - -##### threadId - -The ID of the thread. Can be set to `null` | `undefined` for stateless runs. - -`undefined` | `null` | `string` - -##### runId - -`string` - -The ID of the run. - -##### options? - -Additional options for controlling the stream behavior: - - signal: An AbortSignal that can be used to cancel the stream request - - lastEventId: The ID of the last event received. Can be used to reconnect to a stream without losing events. - - cancelOnDisconnect: When true, automatically cancels the run if the client disconnects from the stream - - streamMode: Controls what types of events to receive from the stream (can be a single mode or array of modes) - Must be a subset of the stream modes passed when creating the run. Background runs default to having the union of all - stream modes enabled. - -`AbortSignal` | \{ `cancelOnDisconnect`: `boolean`; `lastEventId`: `string`; `signal`: `AbortSignal`; `streamMode`: `StreamMode` \| `StreamMode`[]; \} - -#### Returns - -`AsyncGenerator`\<\{ `data`: `any`; `event`: `StreamEvent`; `id`: `string`; \}\> - -An async generator yielding stream parts. - -*** - -### list() - -> **list**(`threadId`, `options`?): `Promise`\<`Run`[]\> - -Defined in: [client.ts:1177](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1177) - -List all runs for a thread. - -#### Parameters - -##### threadId - -`string` - -The ID of the thread. - -##### options? - -Filtering and pagination options. - -###### limit? - -`number` - -Maximum number of runs to return. -Defaults to 10 - -###### offset? - -`number` - -Offset to start from. -Defaults to 0. - -###### status? - -`RunStatus` - -Status of the run to filter by. - -#### Returns - -`Promise`\<`Run`[]\> - -List of runs. - -*** - -### stream() - -Create a run and stream the results. - -#### Param - -The ID of the thread. - -#### Param - -Assistant ID to use for this run. - -#### Param - -Payload for creating a run. - -#### Call Signature - -> **stream**\<`TStreamMode`, `TSubgraphs`\>(`threadId`, `assistantId`, `payload`?): `TypedAsyncGenerator`\<`TStreamMode`, `TSubgraphs`, `TStateType`, `TUpdateType`, `TCustomEventType`\> - -Defined in: [client.ts:917](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L917) - -##### Type Parameters - -• **TStreamMode** *extends* `StreamMode` \| `StreamMode`[] = `StreamMode` - -• **TSubgraphs** *extends* `boolean` = `false` - -##### Parameters - -###### threadId - -`null` - -###### assistantId - -`string` - -###### payload? - -`Omit`\<`RunsStreamPayload`\<`TStreamMode`, `TSubgraphs`\>, `"multitaskStrategy"` \| `"onCompletion"`\> - -##### Returns - -`TypedAsyncGenerator`\<`TStreamMode`, `TSubgraphs`, `TStateType`, `TUpdateType`, `TCustomEventType`\> - -#### Call Signature - -> **stream**\<`TStreamMode`, `TSubgraphs`\>(`threadId`, `assistantId`, `payload`?): `TypedAsyncGenerator`\<`TStreamMode`, `TSubgraphs`, `TStateType`, `TUpdateType`, `TCustomEventType`\> - -Defined in: [client.ts:935](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L935) - -##### Type Parameters - -• **TStreamMode** *extends* `StreamMode` \| `StreamMode`[] = `StreamMode` - -• **TSubgraphs** *extends* `boolean` = `false` - -##### Parameters - -###### threadId - -`string` - -###### assistantId - -`string` - -###### payload? - -`RunsStreamPayload`\<`TStreamMode`, `TSubgraphs`\> - -##### Returns - -`TypedAsyncGenerator`\<`TStreamMode`, `TSubgraphs`, `TStateType`, `TUpdateType`, `TCustomEventType`\> - -*** - -### wait() - -Create a run and wait for it to complete. - -#### Param - -The ID of the thread. - -#### Param - -Assistant ID to use for this run. - -#### Param - -Payload for creating a run. - -#### Call Signature - -> **wait**(`threadId`, `assistantId`, `payload`?): `Promise`\<`DefaultValues`\> - -Defined in: [client.ts:1093](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1093) - -##### Parameters - -###### threadId - -`null` - -###### assistantId - -`string` - -###### payload? - -`Omit`\<`RunsWaitPayload`, `"multitaskStrategy"` \| `"onCompletion"`\> - -##### Returns - -`Promise`\<`DefaultValues`\> - -#### Call Signature - -> **wait**(`threadId`, `assistantId`, `payload`?): `Promise`\<`DefaultValues`\> - -Defined in: [client.ts:1099](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1099) - -##### Parameters - -###### threadId - -`string` - -###### assistantId - -`string` - -###### payload? - -`RunsWaitPayload` - -##### Returns - -`Promise`\<`DefaultValues`\> diff --git a/libs/sdk-js/docs/classes/StoreClient.md b/libs/sdk-js/docs/classes/StoreClient.md deleted file mode 100644 index 97e223da3..000000000 --- a/libs/sdk-js/docs/classes/StoreClient.md +++ /dev/null @@ -1,314 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / StoreClient - -# Class: StoreClient - -Defined in: [client.ts:1347](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1347) - -## Extends - -- `BaseClient` - -## Constructors - -### new StoreClient() - -> **new StoreClient**(`config`?): [`StoreClient`](StoreClient.md) - -Defined in: [client.ts:183](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L183) - -#### Parameters - -##### config? - -[`ClientConfig`](../interfaces/ClientConfig.md) - -#### Returns - -[`StoreClient`](StoreClient.md) - -#### Inherited from - -`BaseClient.constructor` - -## Methods - -### deleteItem() - -> **deleteItem**(`namespace`, `key`): `Promise`\<`void`\> - -Defined in: [client.ts:1468](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1468) - -Delete an item. - -#### Parameters - -##### namespace - -`string`[] - -A list of strings representing the namespace path. - -##### key - -`string` - -The unique identifier for the item. - -#### Returns - -`Promise`\<`void`\> - -Promise - -*** - -### getItem() - -> **getItem**(`namespace`, `key`, `options`?): `Promise`\<`null` \| `Item`\> - -Defined in: [client.ts:1424](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1424) - -Retrieve a single item. - -#### Parameters - -##### namespace - -`string`[] - -A list of strings representing the namespace path. - -##### key - -`string` - -The unique identifier for the item. - -##### options? - -###### refreshTtl? - -`null` \| `boolean` - -Whether to refresh the TTL on this read operation. If null, uses the store's default behavior. - -#### Returns - -`Promise`\<`null` \| `Item`\> - -Promise - -#### Example - -```typescript -const item = await client.store.getItem( - ["documents", "user123"], - "item456", - { refreshTtl: true } -); -console.log(item); -// { -// namespace: ["documents", "user123"], -// key: "item456", -// value: { title: "My Document", content: "Hello World" }, -// createdAt: "2024-07-30T12:00:00Z", -// updatedAt: "2024-07-30T12:00:00Z" -// } -``` - -*** - -### listNamespaces() - -> **listNamespaces**(`options`?): `Promise`\<`ListNamespaceResponse`\> - -Defined in: [client.ts:1564](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1564) - -List namespaces with optional match conditions. - -#### Parameters - -##### options? - -###### limit? - -`number` - -Maximum number of namespaces to return (default is 100). - -###### maxDepth? - -`number` - -Optional integer specifying the maximum depth of namespaces to return. - -###### offset? - -`number` - -Number of namespaces to skip before returning results (default is 0). - -###### prefix? - -`string`[] - -Optional list of strings representing the prefix to filter namespaces. - -###### suffix? - -`string`[] - -Optional list of strings representing the suffix to filter namespaces. - -#### Returns - -`Promise`\<`ListNamespaceResponse`\> - -Promise - -*** - -### putItem() - -> **putItem**(`namespace`, `key`, `value`, `options`?): `Promise`\<`void`\> - -Defined in: [client.ts:1368](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1368) - -Store or update an item. - -#### Parameters - -##### namespace - -`string`[] - -A list of strings representing the namespace path. - -##### key - -`string` - -The unique identifier for the item within the namespace. - -##### value - -`Record`\<`string`, `any`\> - -A dictionary containing the item's data. - -##### options? - -###### index? - -`null` \| `false` \| `string`[] - -Controls search indexing - null (use defaults), false (disable), or list of field paths to index. - -###### ttl? - -`null` \| `number` - -Optional time-to-live in minutes for the item, or null for no expiration. - -#### Returns - -`Promise`\<`void`\> - -Promise - -#### Example - -```typescript -await client.store.putItem( - ["documents", "user123"], - "item456", - { title: "My Document", content: "Hello World" }, - { ttl: 60 } // expires in 60 minutes -); -``` - -*** - -### searchItems() - -> **searchItems**(`namespacePrefix`, `options`?): `Promise`\<`SearchItemsResponse`\> - -Defined in: [client.ts:1519](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L1519) - -Search for items within a namespace prefix. - -#### Parameters - -##### namespacePrefix - -`string`[] - -List of strings representing the namespace prefix. - -##### options? - -###### filter? - -`Record`\<`string`, `any`\> - -Optional dictionary of key-value pairs to filter results. - -###### limit? - -`number` - -Maximum number of items to return (default is 10). - -###### offset? - -`number` - -Number of items to skip before returning results (default is 0). - -###### query? - -`string` - -Optional search query. - -###### refreshTtl? - -`null` \| `boolean` - -Whether to refresh the TTL on items returned by this search. If null, uses the store's default behavior. - -#### Returns - -`Promise`\<`SearchItemsResponse`\> - -Promise - -#### Example - -```typescript -const results = await client.store.searchItems( - ["documents"], - { - filter: { author: "John Doe" }, - limit: 5, - refreshTtl: true - } -); -console.log(results); -// { -// items: [ -// { -// namespace: ["documents", "user123"], -// key: "item789", -// value: { title: "Another Document", author: "John Doe" }, -// createdAt: "2024-07-30T12:00:00Z", -// updatedAt: "2024-07-30T12:00:00Z" -// }, -// // ... additional items ... -// ] -// } -``` diff --git a/libs/sdk-js/docs/classes/ThreadsClient.md b/libs/sdk-js/docs/classes/ThreadsClient.md deleted file mode 100644 index 7dc422c98..000000000 --- a/libs/sdk-js/docs/classes/ThreadsClient.md +++ /dev/null @@ -1,433 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / ThreadsClient - -# Class: ThreadsClient\ - -Defined in: [client.ts:626](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L626) - -## Extends - -- `BaseClient` - -## Type Parameters - -• **TStateType** = `DefaultValues` - -• **TUpdateType** = `TStateType` - -## Constructors - -### new ThreadsClient() - -> **new ThreadsClient**\<`TStateType`, `TUpdateType`\>(`config`?): [`ThreadsClient`](ThreadsClient.md)\<`TStateType`, `TUpdateType`\> - -Defined in: [client.ts:183](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L183) - -#### Parameters - -##### config? - -[`ClientConfig`](../interfaces/ClientConfig.md) - -#### Returns - -[`ThreadsClient`](ThreadsClient.md)\<`TStateType`, `TUpdateType`\> - -#### Inherited from - -`BaseClient.constructor` - -## Methods - -### copy() - -> **copy**(`threadId`): `Promise`\<`Thread`\<`TStateType`\>\> - -Defined in: [client.ts:703](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L703) - -Copy an existing thread - -#### Parameters - -##### threadId - -`string` - -ID of the thread to be copied - -#### Returns - -`Promise`\<`Thread`\<`TStateType`\>\> - -Newly copied thread - -*** - -### create() - -> **create**(`payload`?): `Promise`\<`Thread`\<`TStateType`\>\> - -Defined in: [client.ts:648](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L648) - -Create a new thread. - -#### Parameters - -##### payload? - -Payload for creating a thread. - -###### graphId? - -`string` - -Graph ID to associate with the thread. - -###### ifExists? - -`OnConflictBehavior` - -How to handle duplicate creation. - -**Default** - -```ts -"raise" -``` - -###### metadata? - -`Metadata` - -Metadata for the thread. - -###### supersteps? - -`object`[] - -Apply a list of supersteps when creating a thread, each containing a sequence of updates. - -Used for copying a thread between deployments. - -###### threadId? - -`string` - -ID of the thread to create. - -If not provided, a random UUID will be generated. - -#### Returns - -`Promise`\<`Thread`\<`TStateType`\>\> - -The created thread. - -*** - -### delete() - -> **delete**(`threadId`): `Promise`\<`void`\> - -Defined in: [client.ts:736](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L736) - -Delete a thread. - -#### Parameters - -##### threadId - -`string` - -ID of the thread. - -#### Returns - -`Promise`\<`void`\> - -*** - -### get() - -> **get**\<`ValuesType`\>(`threadId`): `Promise`\<`Thread`\<`ValuesType`\>\> - -Defined in: [client.ts:636](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L636) - -Get a thread by ID. - -#### Type Parameters - -• **ValuesType** = `TStateType` - -#### Parameters - -##### threadId - -`string` - -ID of the thread. - -#### Returns - -`Promise`\<`Thread`\<`ValuesType`\>\> - -The thread. - -*** - -### getHistory() - -> **getHistory**\<`ValuesType`\>(`threadId`, `options`?): `Promise`\<`ThreadState`\<`ValuesType`\>[]\> - -Defined in: [client.ts:888](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L888) - -Get all past states for a thread. - -#### Type Parameters - -• **ValuesType** = `TStateType` - -#### Parameters - -##### threadId - -`string` - -ID of the thread. - -##### options? - -Additional options. - -###### before? - -`Config` - -###### checkpoint? - -`Partial`\<`Omit`\<`Checkpoint`, `"thread_id"`\>\> - -###### limit? - -`number` - -###### metadata? - -`Metadata` - -#### Returns - -`Promise`\<`ThreadState`\<`ValuesType`\>[]\> - -List of thread states. - -*** - -### getState() - -> **getState**\<`ValuesType`\>(`threadId`, `checkpoint`?, `options`?): `Promise`\<`ThreadState`\<`ValuesType`\>\> - -Defined in: [client.ts:795](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L795) - -Get state for a thread. - -#### Type Parameters - -• **ValuesType** = `TStateType` - -#### Parameters - -##### threadId - -`string` - -ID of the thread. - -##### checkpoint? - -`string` | `Checkpoint` - -##### options? - -###### subgraphs? - -`boolean` - -#### Returns - -`Promise`\<`ThreadState`\<`ValuesType`\>\> - -Thread state. - -*** - -### patchState() - -> **patchState**(`threadIdOrConfig`, `metadata`): `Promise`\<`void`\> - -Defined in: [client.ts:858](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L858) - -Patch the metadata of a thread. - -#### Parameters - -##### threadIdOrConfig - -Thread ID or config to patch the state of. - -`string` | `Config` - -##### metadata - -`Metadata` - -Metadata to patch the state with. - -#### Returns - -`Promise`\<`void`\> - -*** - -### search() - -> **search**\<`ValuesType`\>(`query`?): `Promise`\<`Thread`\<`ValuesType`\>[]\> - -Defined in: [client.ts:748](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L748) - -List threads - -#### Type Parameters - -• **ValuesType** = `TStateType` - -#### Parameters - -##### query? - -Query options - -###### limit? - -`number` - -Maximum number of threads to return. -Defaults to 10 - -###### metadata? - -`Metadata` - -Metadata to filter threads by. - -###### offset? - -`number` - -Offset to start from. - -###### sortBy? - -`ThreadSortBy` - -Sort by. - -###### sortOrder? - -`SortOrder` - -Sort order. -Must be one of 'asc' or 'desc'. - -###### status? - -`ThreadStatus` - -Thread status to filter on. - -#### Returns - -`Promise`\<`Thread`\<`ValuesType`\>[]\> - -List of threads - -*** - -### update() - -> **update**(`threadId`, `payload`?): `Promise`\<`Thread`\<`DefaultValues`\>\> - -Defined in: [client.ts:716](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L716) - -Update a thread. - -#### Parameters - -##### threadId - -`string` - -ID of the thread. - -##### payload? - -Payload for updating the thread. - -###### metadata? - -`Metadata` - -Metadata for the thread. - -#### Returns - -`Promise`\<`Thread`\<`DefaultValues`\>\> - -The updated thread. - -*** - -### updateState() - -> **updateState**\<`ValuesType`\>(`threadId`, `options`): `Promise`\<`Pick`\<`Config`, `"configurable"`\>\> - -Defined in: [client.ts:829](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L829) - -Add state to a thread. - -#### Type Parameters - -• **ValuesType** = `TUpdateType` - -#### Parameters - -##### threadId - -`string` - -The ID of the thread. - -##### options - -###### asNode? - -`string` - -###### checkpoint? - -`Checkpoint` - -###### checkpointId? - -`string` - -###### values - -`ValuesType` - -#### Returns - -`Promise`\<`Pick`\<`Config`, `"configurable"`\>\> diff --git a/libs/sdk-js/docs/functions/getApiKey.md b/libs/sdk-js/docs/functions/getApiKey.md deleted file mode 100644 index 8c8e4f1fa..000000000 --- a/libs/sdk-js/docs/functions/getApiKey.md +++ /dev/null @@ -1,32 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / getApiKey - -# Function: getApiKey() - -> **getApiKey**(`apiKey`?): `undefined` \| `string` - -Defined in: [client.ts:122](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L122) - -Get the API key from the environment. -Precedence: - 1. explicit argument - 2. LANGGRAPH_API_KEY - 3. LANGSMITH_API_KEY - 4. LANGCHAIN_API_KEY - -## Parameters - -### apiKey? - -`string` - -Optional API key provided as an argument - -## Returns - -`undefined` \| `string` - -The API key if found, otherwise undefined diff --git a/libs/sdk-js/docs/interfaces/ClientConfig.md b/libs/sdk-js/docs/interfaces/ClientConfig.md deleted file mode 100644 index 38c402010..000000000 --- a/libs/sdk-js/docs/interfaces/ClientConfig.md +++ /dev/null @@ -1,57 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / ClientConfig - -# Interface: ClientConfig - -Defined in: [client.ts:163](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L163) - -## Properties - -### apiKey? - -> `optional` **apiKey**: `string` - -Defined in: [client.ts:165](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L165) - -*** - -### apiUrl? - -> `optional` **apiUrl**: `string` - -Defined in: [client.ts:164](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L164) - -*** - -### callerOptions? - -> `optional` **callerOptions**: `AsyncCallerParams` - -Defined in: [client.ts:166](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L166) - -*** - -### defaultHeaders? - -> `optional` **defaultHeaders**: `Record`\<`string`, `HeaderValue`\> - -Defined in: [client.ts:168](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L168) - -*** - -### onRequest? - -> `optional` **onRequest**: [`RequestHook`](../type-aliases/RequestHook.md) - -Defined in: [client.ts:169](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L169) - -*** - -### timeoutMs? - -> `optional` **timeoutMs**: `number` - -Defined in: [client.ts:167](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L167) diff --git a/libs/sdk-js/docs/react/README.md b/libs/sdk-js/docs/react/README.md deleted file mode 100644 index 0933ca79b..000000000 --- a/libs/sdk-js/docs/react/README.md +++ /dev/null @@ -1,18 +0,0 @@ -**@langchain/langgraph-sdk** - -*** - -# @langchain/langgraph-sdk/react - -## Interfaces - -- [UseStream](interfaces/UseStream.md) -- [UseStreamOptions](interfaces/UseStreamOptions.md) - -## Type Aliases - -- [MessageMetadata](type-aliases/MessageMetadata.md) - -## Functions - -- [useStream](functions/useStream.md) diff --git a/libs/sdk-js/docs/react/functions/useStream.md b/libs/sdk-js/docs/react/functions/useStream.md deleted file mode 100644 index a9cb7d468..000000000 --- a/libs/sdk-js/docs/react/functions/useStream.md +++ /dev/null @@ -1,27 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / useStream - -# Function: useStream() - -> **useStream**\<`StateType`, `Bag`\>(`options`): [`UseStream`](../interfaces/UseStream.md)\<`StateType`, `Bag`\> - -Defined in: [react/stream.tsx:652](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L652) - -## Type Parameters - -• **StateType** *extends* `Record`\<`string`, `unknown`\> = `Record`\<`string`, `unknown`\> - -• **Bag** *extends* `object` = `BagTemplate` - -## Parameters - -### options - -[`UseStreamOptions`](../interfaces/UseStreamOptions.md)\<`StateType`, `Bag`\> - -## Returns - -[`UseStream`](../interfaces/UseStream.md)\<`StateType`, `Bag`\> diff --git a/libs/sdk-js/docs/react/interfaces/UseStream.md b/libs/sdk-js/docs/react/interfaces/UseStream.md deleted file mode 100644 index 06d1c4375..000000000 --- a/libs/sdk-js/docs/react/interfaces/UseStream.md +++ /dev/null @@ -1,227 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / UseStream - -# Interface: UseStream\ - -Defined in: [react/stream.tsx:529](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L529) - -## Type Parameters - -• **StateType** *extends* `Record`\<`string`, `unknown`\> = `Record`\<`string`, `unknown`\> - -• **Bag** *extends* `BagTemplate` = `BagTemplate` - -## Properties - -### assistantId - -> **assistantId**: `string` - -Defined in: [react/stream.tsx:614](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L614) - -The ID of the assistant to use. - -*** - -### branch - -> **branch**: `string` - -Defined in: [react/stream.tsx:564](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L564) - -The current branch of the thread. - -*** - -### client - -> **client**: `Client` - -Defined in: [react/stream.tsx:609](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L609) - -LangGraph SDK client used to send request and receive responses. - -*** - -### error - -> **error**: `unknown` - -Defined in: [react/stream.tsx:541](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L541) - -Last seen error from the thread or during streaming. - -*** - -### experimental\_branchTree - -> **experimental\_branchTree**: `Sequence`\<`StateType`\> - -Defined in: [react/stream.tsx:580](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L580) - -**`Experimental`** - -Tree of all branches for the thread. - -*** - -### getMessagesMetadata() - -> **getMessagesMetadata**: (`message`, `index`?) => `undefined` \| [`MessageMetadata`](../type-aliases/MessageMetadata.md)\<`StateType`\> - -Defined in: [react/stream.tsx:601](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L601) - -Get the metadata for a message, such as first thread state the message -was seen in and branch information. - -#### Parameters - -##### message - -`Message` - -The message to get the metadata for. - -##### index? - -`number` - -The index of the message in the thread. - -#### Returns - -`undefined` \| [`MessageMetadata`](../type-aliases/MessageMetadata.md)\<`StateType`\> - -The metadata for the message. - -*** - -### history - -> **history**: `ThreadState`\<`StateType`\>[] - -Defined in: [react/stream.tsx:574](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L574) - -Flattened history of thread states of a thread. - -*** - -### interrupt - -> **interrupt**: `undefined` \| `Interrupt`\<`GetInterruptType`\<`Bag`\>\> - -Defined in: [react/stream.tsx:585](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L585) - -Get the interrupt value for the stream if interrupted. - -*** - -### isLoading - -> **isLoading**: `boolean` - -Defined in: [react/stream.tsx:546](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L546) - -Whether the stream is currently running. - -*** - -### joinStream() - -> **joinStream**: (`runId`) => `Promise`\<`void`\> - -Defined in: [react/stream.tsx:619](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L619) - -Join an active stream. - -#### Parameters - -##### runId - -`string` - -#### Returns - -`Promise`\<`void`\> - -*** - -### messages - -> **messages**: `Message`[] - -Defined in: [react/stream.tsx:591](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L591) - -Messages inferred from the thread. -Will automatically update with incoming message chunks. - -*** - -### setBranch() - -> **setBranch**: (`branch`) => `void` - -Defined in: [react/stream.tsx:569](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L569) - -Set the branch of the thread. - -#### Parameters - -##### branch - -`string` - -#### Returns - -`void` - -*** - -### stop() - -> **stop**: () => `void` - -Defined in: [react/stream.tsx:551](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L551) - -Stops the stream. - -#### Returns - -`void` - -*** - -### submit() - -> **submit**: (`values`, `options`?) => `void` - -Defined in: [react/stream.tsx:556](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L556) - -Create and stream a run to the thread. - -#### Parameters - -##### values - -`undefined` | `null` | `GetUpdateType`\<`Bag`, `StateType`\> - -##### options? - -`SubmitOptions`\<`StateType`, `GetConfigurableType`\<`Bag`\>\> - -#### Returns - -`void` - -*** - -### values - -> **values**: `StateType` - -Defined in: [react/stream.tsx:536](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L536) - -The current values of the thread. diff --git a/libs/sdk-js/docs/react/interfaces/UseStreamOptions.md b/libs/sdk-js/docs/react/interfaces/UseStreamOptions.md deleted file mode 100644 index 9fbb6f702..000000000 --- a/libs/sdk-js/docs/react/interfaces/UseStreamOptions.md +++ /dev/null @@ -1,343 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / UseStreamOptions - -# Interface: UseStreamOptions\ - -Defined in: [react/stream.tsx:413](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L413) - -## Type Parameters - -• **StateType** *extends* `Record`\<`string`, `unknown`\> = `Record`\<`string`, `unknown`\> - -• **Bag** *extends* `BagTemplate` = `BagTemplate` - -## Properties - -### apiKey? - -> `optional` **apiKey**: `string` - -Defined in: [react/stream.tsx:435](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L435) - -The API key to use. - -*** - -### apiUrl? - -> `optional` **apiUrl**: `string` - -Defined in: [react/stream.tsx:430](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L430) - -The URL of the API to use. - -*** - -### assistantId - -> **assistantId**: `string` - -Defined in: [react/stream.tsx:420](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L420) - -The ID of the assistant to use. - -*** - -### callerOptions? - -> `optional` **callerOptions**: `AsyncCallerParams` - -Defined in: [react/stream.tsx:440](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L440) - -Custom call options, such as custom fetch implementation. - -*** - -### client? - -> `optional` **client**: `Client`\<`DefaultValues`, `DefaultValues`, `unknown`\> - -Defined in: [react/stream.tsx:425](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L425) - -Client used to send requests. - -*** - -### defaultHeaders? - -> `optional` **defaultHeaders**: `Record`\<`string`, `HeaderValue`\> - -Defined in: [react/stream.tsx:445](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L445) - -Default headers to send with requests. - -*** - -### messagesKey? - -> `optional` **messagesKey**: `string` - -Defined in: [react/stream.tsx:453](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L453) - -Specify the key within the state that contains messages. -Defaults to "messages". - -#### Default - -```ts -"messages" -``` - -*** - -### onCreated()? - -> `optional` **onCreated**: (`run`) => `void` - -Defined in: [react/stream.tsx:471](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L471) - -Callback that is called when a new stream is created. - -#### Parameters - -##### run - -`RunCallbackMeta` - -#### Returns - -`void` - -*** - -### onCustomEvent()? - -> `optional` **onCustomEvent**: (`data`, `options`) => `void` - -Defined in: [react/stream.tsx:483](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L483) - -Callback that is called when a custom event is received. - -#### Parameters - -##### data - -`GetCustomEventType`\<`Bag`\> - -##### options - -###### mutate - -(`update`) => `void` - -#### Returns - -`void` - -*** - -### onDebugEvent()? - -> `optional` **onDebugEvent**: (`data`) => `void` - -Defined in: [react/stream.tsx:507](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L507) - -**`Internal`** - -Callback that is called when a debug event is received. - This API is experimental and subject to change. - -#### Parameters - -##### data - -`unknown` - -#### Returns - -`void` - -*** - -### onError()? - -> `optional` **onError**: (`error`, `run`) => `void` - -Defined in: [react/stream.tsx:458](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L458) - -Callback that is called when an error occurs. - -#### Parameters - -##### error - -`unknown` - -##### run - -`undefined` | `RunCallbackMeta` - -#### Returns - -`void` - -*** - -### onFinish()? - -> `optional` **onFinish**: (`state`, `run`) => `void` - -Defined in: [react/stream.tsx:463](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L463) - -Callback that is called when the stream is finished. - -#### Parameters - -##### state - -`ThreadState`\<`StateType`\> - -##### run - -`undefined` | `RunCallbackMeta` - -#### Returns - -`void` - -*** - -### onLangChainEvent()? - -> `optional` **onLangChainEvent**: (`data`) => `void` - -Defined in: [react/stream.tsx:501](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L501) - -Callback that is called when a LangChain event is received. - -#### Parameters - -##### data - -###### data - -`unknown` - -###### event - -`string` & `object` \| `"on_tool_start"` \| `"on_tool_stream"` \| `"on_tool_end"` \| `"on_chat_model_start"` \| `"on_chat_model_stream"` \| `"on_chat_model_end"` \| `"on_llm_start"` \| `"on_llm_stream"` \| `"on_llm_end"` \| `"on_chain_start"` \| `"on_chain_stream"` \| `"on_chain_end"` \| `"on_retriever_start"` \| `"on_retriever_stream"` \| `"on_retriever_end"` \| `"on_prompt_start"` \| `"on_prompt_stream"` \| `"on_prompt_end"` - -###### metadata - -`Record`\<`string`, `unknown`\> - -###### name - -`string` - -###### parent_ids - -`string`[] - -###### run_id - -`string` - -###### tags - -`string`[] - -#### Returns - -`void` - -#### See - -https://langchain-ai.github.io/langgraph/cloud/how-tos/stream_events/#stream-graph-in-events-mode for more details. - -*** - -### onMetadataEvent()? - -> `optional` **onMetadataEvent**: (`data`) => `void` - -Defined in: [react/stream.tsx:495](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L495) - -Callback that is called when a metadata event is received. - -#### Parameters - -##### data - -###### run_id - -`string` - -###### thread_id - -`string` - -#### Returns - -`void` - -*** - -### onThreadId()? - -> `optional` **onThreadId**: (`threadId`) => `void` - -Defined in: [react/stream.tsx:517](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L517) - -Callback that is called when the thread ID is updated (ie when a new thread is created). - -#### Parameters - -##### threadId - -`string` - -#### Returns - -`void` - -*** - -### onUpdateEvent()? - -> `optional` **onUpdateEvent**: (`data`) => `void` - -Defined in: [react/stream.tsx:476](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L476) - -Callback that is called when an update event is received. - -#### Parameters - -##### data - -#### Returns - -`void` - -*** - -### reconnectOnMount? - -> `optional` **reconnectOnMount**: `boolean` \| () => `RunMetadataStorage` - -Defined in: [react/stream.tsx:520](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L520) - -Will reconnect the stream on mount - -*** - -### threadId? - -> `optional` **threadId**: `null` \| `string` - -Defined in: [react/stream.tsx:512](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L512) - -The ID of the thread to fetch history and current values from. diff --git a/libs/sdk-js/docs/react/type-aliases/MessageMetadata.md b/libs/sdk-js/docs/react/type-aliases/MessageMetadata.md deleted file mode 100644 index 9496a7913..000000000 --- a/libs/sdk-js/docs/react/type-aliases/MessageMetadata.md +++ /dev/null @@ -1,42 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / MessageMetadata - -# Type Alias: MessageMetadata\ - -> **MessageMetadata**\<`StateType`\>: `object` - -Defined in: [react/stream.tsx:169](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/react/stream.tsx#L169) - -## Type Parameters - -• **StateType** *extends* `Record`\<`string`, `unknown`\> - -## Type declaration - -### branch - -> **branch**: `string` \| `undefined` - -The branch of the message. - -### branchOptions - -> **branchOptions**: `string`[] \| `undefined` - -The list of branches this message is part of. -This is useful for displaying branching controls. - -### firstSeenState - -> **firstSeenState**: `ThreadState`\<`StateType`\> \| `undefined` - -The first thread state the message was seen in. - -### messageId - -> **messageId**: `string` - -The ID of the message used. diff --git a/libs/sdk-js/docs/type-aliases/RequestHook.md b/libs/sdk-js/docs/type-aliases/RequestHook.md deleted file mode 100644 index 53d535e2e..000000000 --- a/libs/sdk-js/docs/type-aliases/RequestHook.md +++ /dev/null @@ -1,25 +0,0 @@ -[**@langchain/langgraph-sdk**](../README.md) - -*** - -[@langchain/langgraph-sdk](../README.md) / RequestHook - -# Type Alias: RequestHook() - -> **RequestHook**: (`url`, `init`) => `Promise`\<`RequestInit`\> \| `RequestInit` - -Defined in: [client.ts:158](https://github.com/langchain-ai/langgraph/blob/a7ea5e44ce12e3618d1a766587163afbfe424e04/libs/sdk-js/src/client.ts#L158) - -## Parameters - -### url - -`URL` - -### init - -`RequestInit` - -## Returns - -`Promise`\<`RequestInit`\> \| `RequestInit` diff --git a/libs/sdk-js/index.cjs b/libs/sdk-js/index.cjs deleted file mode 100644 index c84256f7a..000000000 --- a/libs/sdk-js/index.cjs +++ /dev/null @@ -1 +0,0 @@ -module.exports = require('./dist/index.cjs'); \ No newline at end of file diff --git a/libs/sdk-js/index.d.cts b/libs/sdk-js/index.d.cts deleted file mode 100644 index 485d17256..000000000 --- a/libs/sdk-js/index.d.cts +++ /dev/null @@ -1 +0,0 @@ -export * from './dist/index.js' \ No newline at end of file diff --git a/libs/sdk-js/index.d.ts b/libs/sdk-js/index.d.ts deleted file mode 100644 index 485d17256..000000000 --- a/libs/sdk-js/index.d.ts +++ /dev/null @@ -1 +0,0 @@ -export * from './dist/index.js' \ No newline at end of file diff --git a/libs/sdk-js/index.js b/libs/sdk-js/index.js deleted file mode 100644 index 485d17256..000000000 --- a/libs/sdk-js/index.js +++ /dev/null @@ -1 +0,0 @@ -export * from './dist/index.js' \ No newline at end of file diff --git a/libs/sdk-js/react.cjs b/libs/sdk-js/react.cjs deleted file mode 100644 index 86a38b8c3..000000000 --- a/libs/sdk-js/react.cjs +++ /dev/null @@ -1 +0,0 @@ -module.exports = require('./dist/react/index.cjs'); \ No newline at end of file diff --git a/libs/sdk-js/react.d.cts b/libs/sdk-js/react.d.cts deleted file mode 100644 index fd96c4787..000000000 --- a/libs/sdk-js/react.d.cts +++ /dev/null @@ -1 +0,0 @@ -export * from './dist/react/index.js' \ No newline at end of file diff --git a/libs/sdk-js/react.d.ts b/libs/sdk-js/react.d.ts deleted file mode 100644 index fd96c4787..000000000 --- a/libs/sdk-js/react.d.ts +++ /dev/null @@ -1 +0,0 @@ -export * from './dist/react/index.js' \ No newline at end of file diff --git a/libs/sdk-js/react.js b/libs/sdk-js/react.js deleted file mode 100644 index fd96c4787..000000000 --- a/libs/sdk-js/react.js +++ /dev/null @@ -1 +0,0 @@ -export * from './dist/react/index.js' \ No newline at end of file From 1f1d032430a29800709929890cebf445615ab0c0 Mon Sep 17 00:00:00 2001 From: lc-arjun Date: Tue, 8 Jul 2025 06:59:02 -0700 Subject: [PATCH 26/31] docs: add tunnel flag to node server docs (#5398) --- docs/docs/troubleshooting/studio.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/docs/troubleshooting/studio.md b/docs/docs/troubleshooting/studio.md index b06f325de..3fcbdea7f 100644 --- a/docs/docs/troubleshooting/studio.md +++ b/docs/docs/troubleshooting/studio.md @@ -17,7 +17,7 @@ Safari blocks plain-HTTP traffic on localhost. When running Studio with `langgra ```shell # Requires @langchain/langgraph-cli>=0.0.26 - npx @langchain/langgraph-cli dev + npx @langchain/langgraph-cli dev --tunnel ``` The command outputs a URL in this format: @@ -55,7 +55,7 @@ Disable Brave Shields for LangSmith using the Brave icon in the URL bar. ```shell # Requires @langchain/langgraph-cli>=0.0.26 - npx @langchain/langgraph-cli dev + npx @langchain/langgraph-cli dev --tunnel ``` The command outputs a URL in this format: From f21fc056bff70d7e5c8dd7553577a1a26238713a Mon Sep 17 00:00:00 2001 From: Lauren Hirata Singh Date: Tue, 8 Jul 2025 10:36:47 -0400 Subject: [PATCH 27/31] docs: Convert example notebooks (#5381) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Agentic RAG * Fix formatting * Agent supervisor * fix links * SQL agent * Graph Runs in LS * fix format * Autogen + LG tutorial * fixes * update sql * remove old notebooks * docs: Add section about data region for LGP data plane (#5378) Add section about data region. * fix: remove empty notebook (#5379) * Fix docstring for _unset_config_context function (#5374) Signed-off-by: jitokim * Fix typo in StreamMode debug description: checlkpoints → checkpoints (#5371) Signed-off-by: jitokim * fix: remove unused import in generate_llms_text.py (#5380) * dcos: Fix deprecation of TavilySearch (#5375) Fix deprecation: The class `TavilySearchResults` was deprecated in LangChain 0.3.25 and will be removed in 1.0 * Fix typo: funtion → function (#5370) fix typos Signed-off-by: jitokim * docs: feedback edits (#5387) * docs: update lgp deployment metric list (#5388) * chore(deps): bump peter-evans/create-pull-request from 6 to 7 (#5365) Bumps [peter-evans/create-pull-request](https://github.com/peter-evans/create-pull-request) from 6 to 7. - [Release notes](https://github.com/peter-evans/create-pull-request/releases) - [Commits](https://github.com/peter-evans/create-pull-request/compare/v6...v7) --- updated-dependencies: - dependency-name: peter-evans/create-pull-request dependency-version: '7' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * docs: correct link in docs/docs/how-tos/graph-api.md (#5377) Update graph-api.md * docs: Update quick_start.md Rest API Guide (#5368) Update quick_start.md Rest API Guide The curl command in the quick start needs some minor changes to work out of the box. I hope by adding these changes then new users can get started more quickly * Remove duplicate CONFIG_KEY_CHECKPOINT_MAP from RESERVED set (#5372) Signed-off-by: jitokim * docs: fix typo in persistence (#5329) * docs: fix typo in application_structure * docs: fix typo in persistence * chore[deps]: upgrade dependencies with `uv lock --upgrade` (#5358) * chore: upgrade dependencies with `uv lock --upgrade` * linting * upgrade PR title --------- Co-authored-by: sydney-runkle <54324534+sydney-runkle@users.noreply.github.com> Co-authored-by: Sydney Runkle * Updated examples for SummarizationNode to account for serde with persistence layers (#5257) * docs: move script into scripts (#5384) * docs: update sql tutorial (#5389) --------- Signed-off-by: jitokim Signed-off-by: dependabot[bot] Co-authored-by: Andrew Nguonly Co-authored-by: Michael Li Co-authored-by: jito Co-authored-by: Serhii Polishchuk Co-authored-by: hari-dhanushkodi Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Fadel Akram Co-authored-by: David <31293924+dreadn0ught@users.noreply.github.com> Co-authored-by: Youssef Ahmed Mohamed Abdelrahman <109446360+unauthorised-401@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: sydney-runkle <54324534+sydney-runkle@users.noreply.github.com> Co-authored-by: Sydney Runkle Co-authored-by: Nick Riley Co-authored-by: Eugene Yurtsev Co-authored-by: ccurme --- CONTRIBUTING.md | 2 +- docs/docs/concepts/multi_agent.md | 4 +- .../410e0089-2ab8-46bb-a61a-827187fd46b3.png | Bin 0 -> 75818 bytes docs/docs/how-tos/assets/autogen-output.png | Bin 0 -> 5919 bytes .../d38d1f2b-0f4c-4707-b531-a3c749de987f.png | Bin 0 -> 53669 bytes docs/docs/how-tos/autogen-integration.ipynb | 406 ----- docs/docs/how-tos/autogen-integration.md | 321 ++++ .../how-tos/autogen-langgraph-platform.ipynb | 171 -- docs/docs/how-tos/multi_agent.ipynb | 657 -------- docs/docs/how-tos/multi_agent.md | 2 +- docs/docs/how-tos/run-id-langsmith.ipynb | 274 ---- docs/docs/how-tos/run-id-langsmith.md | 155 ++ .../multi_agent/agent_supervisor.ipynb | 1403 ----------------- .../tutorials/multi_agent/agent_supervisor.md | 812 ++++++++++ .../tutorials/multi_agent/assets/diagram.png | Bin 0 -> 25505 bytes .../multi_agent/assets/multi-output.png | Bin 0 -> 14338 bytes .../tutorials/multi_agent/assets/output.png | Bin 0 -> 14338 bytes docs/docs/tutorials/overview.md | 8 +- .../rag/assets/agentic-rag-output.png | Bin 0 -> 81761 bytes .../screenshot_2024_02_14_3_43_58_pm.png | Bin 0 -> 104631 bytes .../tutorials/rag/langgraph_agentic_rag.ipynb | 928 ----------- .../tutorials/rag/langgraph_agentic_rag.md | 522 ++++++ docs/docs/tutorials/sql/output.png | Bin 0 -> 20460 bytes docs/docs/tutorials/sql/sql-agent.md | 549 +++++++ docs/mkdocs.yml | 11 +- 25 files changed, 2372 insertions(+), 3853 deletions(-) create mode 100644 docs/docs/how-tos/assets/410e0089-2ab8-46bb-a61a-827187fd46b3.png create mode 100644 docs/docs/how-tos/assets/autogen-output.png create mode 100644 docs/docs/how-tos/assets/d38d1f2b-0f4c-4707-b531-a3c749de987f.png delete mode 100644 docs/docs/how-tos/autogen-integration.ipynb create mode 100644 docs/docs/how-tos/autogen-integration.md delete mode 100644 docs/docs/how-tos/autogen-langgraph-platform.ipynb delete mode 100644 docs/docs/how-tos/multi_agent.ipynb delete mode 100644 docs/docs/how-tos/run-id-langsmith.ipynb create mode 100644 docs/docs/how-tos/run-id-langsmith.md delete mode 100644 docs/docs/tutorials/multi_agent/agent_supervisor.ipynb create mode 100644 docs/docs/tutorials/multi_agent/agent_supervisor.md create mode 100644 docs/docs/tutorials/multi_agent/assets/diagram.png create mode 100644 docs/docs/tutorials/multi_agent/assets/multi-output.png create mode 100644 docs/docs/tutorials/multi_agent/assets/output.png create mode 100644 docs/docs/tutorials/rag/assets/agentic-rag-output.png create mode 100644 docs/docs/tutorials/rag/assets/screenshot_2024_02_14_3_43_58_pm.png delete mode 100644 docs/docs/tutorials/rag/langgraph_agentic_rag.ipynb create mode 100644 docs/docs/tutorials/rag/langgraph_agentic_rag.md create mode 100644 docs/docs/tutorials/sql/output.png create mode 100644 docs/docs/tutorials/sql/sql-agent.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index a4e9a3eca..48fd5593f 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -60,7 +60,7 @@ In LangGraph, these are often higher level guides that show off end-to-end use c Some examples include: - [Build a Customer Support Bot](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/) -- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/) +- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql/sql-agent/) Here are some high-level tips on writing a good tutorial: diff --git a/docs/docs/concepts/multi_agent.md b/docs/docs/concepts/multi_agent.md index 81967f610..5d3e30a29 100644 --- a/docs/docs/concepts/multi_agent.md +++ b/docs/docs/concepts/multi_agent.md @@ -26,7 +26,7 @@ The primary benefits of using multi-agent systems are: There are several ways to connect agents in a multi-agent system: - **Network**: each agent can communicate with [every other agent](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/multi-agent-collaboration/). Any agent can decide which other agent to call next. -- **Supervisor**: each agent communicates with a single [supervisor](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/) agent. Supervisor agent makes decisions on which agent should be called next. +- **Supervisor**: each agent communicates with a single [supervisor](../tutorials/multi_agent/agent_supervisor.md) agent. Supervisor agent makes decisions on which agent should be called next. - **Supervisor (tool-calling)**: this is a special case of supervisor architecture. Individual agents can be represented as tools. In this case, a supervisor agent uses a tool-calling LLM to decide which of the agent tools to call, as well as the arguments to pass to those agents. - **Hierarchical**: you can define a multi-agent system with [a supervisor of supervisors](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/). This is a generalization of the supervisor architecture and allows for more complex control flows. - **Custom multi-agent workflow**: each agent communicates with only a subset of agents. Parts of the flow are deterministic, and only some agents can decide which other agents to call next. @@ -211,7 +211,7 @@ builder.add_edge(START, "supervisor") supervisor = builder.compile() ``` -Check out this [tutorial](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/) for an example of supervisor multi-agent architecture. +Check out this [tutorial](../tutorials/multi_agent/agent_supervisor.md) for an example of supervisor multi-agent architecture. ### Supervisor (tool-calling) diff --git a/docs/docs/how-tos/assets/410e0089-2ab8-46bb-a61a-827187fd46b3.png b/docs/docs/how-tos/assets/410e0089-2ab8-46bb-a61a-827187fd46b3.png new file mode 100644 index 0000000000000000000000000000000000000000..9bd66e5654ae1f2d93b2329d3a37bcf853a93177 GIT binary patch literal 75818 zcmd?RQ*>nC_6FKXCmnPrskmd?wr$%^$F^;=V^qgh$F^6ExV)?w94sd6r%#{YB*cXkKYjYF|LGI>7&Q1l-)M-=fc|}U6qHbg zhK63*B2o8_lJL9iGmR3PkR2(9sl|=F47M=jFI5~p85A} 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a/docs/docs/how-tos/autogen-integration.ipynb +++ /dev/null @@ -1,406 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "100c0c81-6a9f-4ba1-b1a8-42aae82b7172", - "metadata": {}, - "source": [ - "# How to integrate LangGraph with AutoGen, CrewAI, and other frameworks\n", - "\n", - "LangGraph is a framework for building agentic and multi-agent applications. LangGraph can be easily integrated with other agent frameworks. \n", - "\n", - "The primary reasons you might want to integrate LangGraph with other agent frameworks:\n", - "\n", - "- create [multi-agent systems](../../concepts/multi_agent) where individual agents are built with different frameworks\n", - "- leverage LangGraph to add features like [persistence](../../concepts/persistence), [streaming](../../concepts/streaming), [short and long-term memory](../../concepts/memory) and more\n", - "\n", - "The simplest way to integrate agents from other frameworks is by calling those agents inside a LangGraph [node](../../concepts/low_level/#nodes):\n", - "\n", - "```python\n", - "from langgraph.graph import StateGraph, MessagesState, START\n", - "\n", - "autogen_agent = autogen.AssistantAgent(name=\"assistant\", ...)\n", - "user_proxy = autogen.UserProxyAgent(name=\"user_proxy\", ...)\n", - "\n", - "def call_autogen_agent(state: MessagesState):\n", - " response = user_proxy.initiate_chat(\n", - " autogen_agent,\n", - " message=state[\"messages\"][-1],\n", - " ...\n", - " )\n", - " ...\n", - "\n", - "graph = (\n", - " StateGraph(MessagesState)\n", - " .add_node(call_autogen_agent)\n", - " .add_edge(START, \"call_autogen_agent\")\n", - " .compile()\n", - ")\n", - "\n", - "graph.invoke({\n", - " \"messages\": [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"Find numbers between 10 and 30 in fibonacci sequence\",\n", - " }\n", - " ]\n", - "})\n", - "```\n", - "\n", - "In this guide we show how to build a LangGraph chatbot that integrates with AutoGen, but you can follow the same approach with other frameworks." - ] - }, - { - "cell_type": "markdown", - "id": "b189ceb2-132b-4c7b-81b4-c7b8b062f833", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "62417d3a-94f9-4a52-9962-12639d714966", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install autogen langgraph" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d46da41d-0a71-4654-aec8-9e6ad8765236", - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "OPENAI_API_KEY: ········\n" - ] - } - ], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "\n", - "def _set_env(var: str):\n", - " if not os.environ.get(var):\n", - " os.environ[var] = getpass.getpass(f\"{var}: \")\n", - "\n", - "\n", - "_set_env(\"OPENAI_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "1926bbc3-6b06-41e0-9604-860a2bbf8fa3", - "metadata": {}, - "source": [ - "## Define AutoGen agent\n", - "\n", - "Here we define our AutoGen agent. Adapted from official tutorial [here](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_web_info.ipynb)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "524de117-ff09-4b26-bfe8-a9f85a46ffd5", - "metadata": {}, - "outputs": [], - "source": [ - "import autogen\n", - "import os\n", - "\n", - "config_list = [{\"model\": \"gpt-4o\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]\n", - "\n", - "llm_config = {\n", - " \"timeout\": 600,\n", - " \"cache_seed\": 42,\n", - " \"config_list\": config_list,\n", - " \"temperature\": 0,\n", - "}\n", - "\n", - "autogen_agent = autogen.AssistantAgent(\n", - " name=\"assistant\",\n", - " llm_config=llm_config,\n", - ")\n", - "\n", - "user_proxy = autogen.UserProxyAgent(\n", - " name=\"user_proxy\",\n", - " human_input_mode=\"NEVER\",\n", - " max_consecutive_auto_reply=10,\n", - " is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n", - " code_execution_config={\n", - " \"work_dir\": \"web\",\n", - " \"use_docker\": False,\n", - " }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n", - " llm_config=llm_config,\n", - " system_message=\"Reply TERMINATE if the task has been solved at full satisfaction. Otherwise, reply CONTINUE, or the reason why the task is not solved yet.\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "8aa858e2-4acb-4f75-be20-b9ccbbcb5073", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "dcc478f5-4a35-43f8-bf59-9cb71289cd00", - "metadata": {}, - "source": [ - "## Create the graph\n", - "\n", - "We will now create a LangGraph chatbot graph that calls AutoGen agent." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d129e4e1-3766-429a-b806-cde3d8bc0469", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_core.messages import convert_to_openai_messages\n", - "from langgraph.graph import StateGraph, MessagesState, START\n", - "from langgraph.checkpoint.memory import MemorySaver\n", - "\n", - "\n", - "def call_autogen_agent(state: MessagesState):\n", - " # convert to openai-style messages\n", - " messages = convert_to_openai_messages(state[\"messages\"])\n", - " response = user_proxy.initiate_chat(\n", - " autogen_agent,\n", - " message=messages[-1],\n", - " # pass previous message history as context\n", - " carryover=messages[:-1],\n", - " )\n", - " # get the final response from the agent\n", - " content = response.chat_history[-1][\"content\"]\n", - " return {\"messages\": {\"role\": \"assistant\", \"content\": content}}\n", - "\n", - "\n", - "# add short-term memory for storing conversation history\n", - "checkpointer = MemorySaver()\n", - "\n", - "builder = StateGraph(MessagesState)\n", - "builder.add_node(call_autogen_agent)\n", - "builder.add_edge(START, \"call_autogen_agent\")\n", - "graph = builder.compile(checkpointer=checkpointer)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c761fc05-e8b6-4905-a793-eb7522d20060", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import display, Image\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "23d629c3-1d6b-40af-adf6-915e15657566", - "metadata": {}, - "source": [ - "## Run the graph\n", - "\n", - "We can now run the graph." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a279b667-0f5d-4008-8d43-c806a3f379c4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001B[33muser_proxy\u001B[0m (to assistant):\n", - "\n", - "Find numbers between 10 and 30 in fibonacci sequence\n", - "\n", - "--------------------------------------------------------------------------------\n", - "\u001B[33massistant\u001B[0m (to user_proxy):\n", - "\n", - "To find numbers between 10 and 30 in the Fibonacci sequence, we can generate the Fibonacci sequence and check which numbers fall within this range. Here's a plan:\n", - "\n", - "1. Generate Fibonacci numbers starting from 0.\n", - "2. Continue generating until the numbers exceed 30.\n", - "3. Collect and print the numbers that are between 10 and 30.\n", - "\n", - "Let's implement this in Python:\n", - "\n", - "```python\n", - "# filename: fibonacci_range.py\n", - "\n", - "def fibonacci_sequence():\n", - " a, b = 0, 1\n", - " while a <= 30:\n", - " if 10 <= a <= 30:\n", - " print(a)\n", - " a, b = b, a + b\n", - "\n", - "fibonacci_sequence()\n", - "```\n", - "\n", - "This script will print the Fibonacci numbers between 10 and 30. Please execute the code to see the result.\n", - "\n", - "--------------------------------------------------------------------------------\n", - "\u001B[31m\n", - ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001B[0m\n", - "\u001B[33muser_proxy\u001B[0m (to assistant):\n", - "\n", - "exitcode: 0 (execution succeeded)\n", - "Code output: \n", - "13\n", - "21\n", - "\n", - "\n", - "--------------------------------------------------------------------------------\n", - "\u001B[33massistant\u001B[0m (to user_proxy):\n", - "\n", - "The Fibonacci numbers between 10 and 30 are 13 and 21. \n", - "\n", - "These numbers are part of the Fibonacci sequence, which is generated by adding the two preceding numbers to get the next number, starting from 0 and 1. \n", - "\n", - "The sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...\n", - "\n", - "As you can see, 13 and 21 are the only numbers in this sequence that fall between 10 and 30.\n", - "\n", - "TERMINATE\n", - "\n", - "--------------------------------------------------------------------------------\n", - "{'call_autogen_agent': {'messages': {'role': 'assistant', 'content': 'The Fibonacci numbers between 10 and 30 are 13 and 21. \\n\\nThese numbers are part of the Fibonacci sequence, which is generated by adding the two preceding numbers to get the next number, starting from 0 and 1. \\n\\nThe sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...\\n\\nAs you can see, 13 and 21 are the only numbers in this sequence that fall between 10 and 30.\\n\\nTERMINATE'}}}\n" - ] - } - ], - "source": [ - "# pass the thread ID to persist agent outputs for future interactions\n", - "# highlight-next-line\n", - "config = {\"configurable\": {\"thread_id\": \"1\"}}\n", - "\n", - "for chunk in graph.stream(\n", - " {\n", - " \"messages\": [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"Find numbers between 10 and 30 in fibonacci sequence\",\n", - " }\n", - " ]\n", - " },\n", - " # highlight-next-line\n", - " config,\n", - "):\n", - " print(chunk)" - ] - }, - { - "cell_type": "markdown", - "id": "c6cd57b4-d4ee-49f6-be12-318613849669", - "metadata": {}, - "source": [ - "Since we're leveraging LangGraph's [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/) features we can now continue the conversation using the same thread ID -- LangGraph will automatically pass previous history to the AutoGen agent:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e68811a7-962e-4fe3-9f45-9b99ebbe04e7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001B[33muser_proxy\u001B[0m (to assistant):\n", - "\n", - "Multiply the last number by 3\n", - "Context: \n", - "Find numbers between 10 and 30 in fibonacci sequence\n", - "The Fibonacci numbers between 10 and 30 are 13 and 21. \n", - "\n", - "These numbers are part of the Fibonacci sequence, which is generated by adding the two preceding numbers to get the next number, starting from 0 and 1. \n", - "\n", - "The sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...\n", - "\n", - "As you can see, 13 and 21 are the only numbers in this sequence that fall between 10 and 30.\n", - "\n", - "TERMINATE\n", - "\n", - "--------------------------------------------------------------------------------\n", - "\u001B[33massistant\u001B[0m (to user_proxy):\n", - "\n", - "The last number in the Fibonacci sequence between 10 and 30 is 21. Multiplying 21 by 3 gives:\n", - "\n", - "21 * 3 = 63\n", - "\n", - "TERMINATE\n", - "\n", - "--------------------------------------------------------------------------------\n", - "{'call_autogen_agent': {'messages': {'role': 'assistant', 'content': 'The last number in the Fibonacci sequence between 10 and 30 is 21. Multiplying 21 by 3 gives:\\n\\n21 * 3 = 63\\n\\nTERMINATE'}}}\n" - ] - } - ], - "source": [ - "for chunk in graph.stream(\n", - " {\n", - " \"messages\": [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"Multiply the last number by 3\",\n", - " }\n", - " ]\n", - " },\n", - " # highlight-next-line\n", - " config,\n", - "):\n", - " print(chunk)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/docs/how-tos/autogen-integration.md b/docs/docs/how-tos/autogen-integration.md new file mode 100644 index 000000000..efbcf3d30 --- /dev/null +++ b/docs/docs/how-tos/autogen-integration.md @@ -0,0 +1,321 @@ +# How to integrate LangGraph with AutoGen, CrewAI, and other frameworks + +This guide shows how to integrate AutoGen agents with LangGraph to leverage features like persistence, streaming, and memory, and then deploy the integrated solution to LangGraph Platform for scalable production use. In this guide we show how to build a LangGraph chatbot that integrates with AutoGen, but you can follow the same approach with other frameworks. + +Integrating AutoGen with LangGraph provides several benefits: + +- Enhanced features: Add [persistence](../concepts/persistence.md), [streaming](../concepts/streaming.md), [short and long-term memory](../concepts/memory.md) and more to your AutoGen agents. +- Multi-agent systems: Build [multi-agent systems](../concepts/multi_agent.md) where individual agents are built with different frameworks. +- Production deployment: Deploy your integrated solution to [LangGraph Platform](../concepts/langgraph_platform.md) for scalable production use. + +## Prerequisites + +- Python 3.9+ +- Autogen: `pip install autogen` +- LangGraph: `pip install langgraph` +- OpenAI API key + +## Setup + +Set your your environment: + +```python +import getpass +import os + + +def _set_env(var: str): + if not os.environ.get(var): + os.environ[var] = getpass.getpass(f"{var}: ") + + +_set_env("OPENAI_API_KEY") +``` + +## 1. Define AutoGen agent + +Create an AutoGen agent that can execute code. This example is adapted from AutoGen's [official tutorials](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_web_info.ipynb): + +```python +import autogen +import os + +config_list = [{"model": "gpt-4o", "api_key": os.environ["OPENAI_API_KEY"]}] + +llm_config = { + "timeout": 600, + "cache_seed": 42, + "config_list": config_list, + "temperature": 0, +} + +autogen_agent = autogen.AssistantAgent( + name="assistant", + llm_config=llm_config, +) + +user_proxy = autogen.UserProxyAgent( + name="user_proxy", + human_input_mode="NEVER", + max_consecutive_auto_reply=10, + is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"), + code_execution_config={ + "work_dir": "web", + "use_docker": False, + }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly. + llm_config=llm_config, + system_message="Reply TERMINATE if the task has been solved at full satisfaction. Otherwise, reply CONTINUE, or the reason why the task is not solved yet.", +) +``` + +## 2. Create the graph + +We will now create a LangGraph chatbot graph that calls AutoGen agent. + +```python +from langchain_core.messages import convert_to_openai_messages +from langgraph.graph import StateGraph, MessagesState, START +from langgraph.checkpoint.memory import MemorySaver + +def call_autogen_agent(state: MessagesState): + # Convert LangGraph messages to OpenAI format for AutoGen + messages = convert_to_openai_messages(state["messages"]) + + # Get the last user message + last_message = messages[-1] + + # Pass previous message history as context (excluding the last message) + carryover = messages[:-1] if len(messages) > 1 else [] + + # Initiate chat with AutoGen + response = user_proxy.initiate_chat( + autogen_agent, + message=last_message, + carryover=carryover + ) + + # Extract the final response from the agent + final_content = response.chat_history[-1]["content"] + + # Return the response in LangGraph format + return {"messages": {"role": "assistant", "content": final_content}} + +# Create the graph with memory for persistence +checkpointer = MemorySaver() + +# Build the graph +builder = StateGraph(MessagesState) +builder.add_node("autogen", call_autogen_agent) +builder.add_edge(START, "autogen") + +# Compile with checkpointer for persistence +graph = builder.compile(checkpointer=checkpointer) +``` + +```python +from IPython.display import display, Image + +display(Image(graph.get_graph().draw_mermaid_png())) +``` + +![Graph](./assets/autogen-output.png) + +## 3. Test the graph locally + +Before deploying to LangGraph Platform, you can test the graph locally: + +```python +# pass the thread ID to persist agent outputs for future interactions +# highlight-next-line +config = {"configurable": {"thread_id": "1"}} + +for chunk in graph.stream( + { + "messages": [ + { + "role": "user", + "content": "Find numbers between 10 and 30 in fibonacci sequence", + } + ] + }, + # highlight-next-line + config, +): + print(chunk) +``` + +**Output:** +``` +user_proxy (to assistant): + +Find numbers between 10 and 30 in fibonacci sequence + +-------------------------------------------------------------------------------- +assistant (to user_proxy): + +To find numbers between 10 and 30 in the Fibonacci sequence, we can generate the Fibonacci sequence and check which numbers fall within this range. Here's a plan: + +1. Generate Fibonacci numbers starting from 0. +2. Continue generating until the numbers exceed 30. +3. Collect and print the numbers that are between 10 and 30. + +... +``` + +Since we're leveraging LangGraph's [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/) features we can now continue the conversation using the same thread ID -- LangGraph will automatically pass previous history to the AutoGen agent: + +```python +for chunk in graph.stream( + { + "messages": [ + { + "role": "user", + "content": "Multiply the last number by 3", + } + ] + }, + # highlight-next-line + config, +): + print(chunk) +``` + +**Output:** +``` +user_proxy (to assistant): + +Multiply the last number by 3 +Context: +Find numbers between 10 and 30 in fibonacci sequence +The Fibonacci numbers between 10 and 30 are 13 and 21. + +These numbers are part of the Fibonacci sequence, which is generated by adding the two preceding numbers to get the next number, starting from 0 and 1. + +The sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ... + +As you can see, 13 and 21 are the only numbers in this sequence that fall between 10 and 30. + +TERMINATE + +-------------------------------------------------------------------------------- +assistant (to user_proxy): + +The last number in the Fibonacci sequence between 10 and 30 is 21. Multiplying 21 by 3 gives: + +21 * 3 = 63 + +TERMINATE + +-------------------------------------------------------------------------------- +{'call_autogen_agent': {'messages': {'role': 'assistant', 'content': 'The last number in the Fibonacci sequence between 10 and 30 is 21. Multiplying 21 by 3 gives:\n\n21 * 3 = 63\n\nTERMINATE'}}} +``` + +## 4. Prepare for deployment + +To deploy to LangGraph Platform, create a file structure like the following: + +``` +my-autogen-agent/ +├── agent.py # Your main agent code +├── requirements.txt # Python dependencies +└── langgraph.json # LangGraph configuration +``` + +=== "agent.py" + + ```python + import os + import autogen + from langchain_core.messages import convert_to_openai_messages + from langgraph.graph import StateGraph, MessagesState, START + from langgraph.checkpoint.memory import MemorySaver + + # AutoGen configuration + config_list = [{"model": "gpt-4o", "api_key": os.environ["OPENAI_API_KEY"]}] + + llm_config = { + "timeout": 600, + "cache_seed": 42, + "config_list": config_list, + "temperature": 0, + } + + # Create AutoGen agents + autogen_agent = autogen.AssistantAgent( + name="assistant", + llm_config=llm_config, + ) + + user_proxy = autogen.UserProxyAgent( + name="user_proxy", + human_input_mode="NEVER", + max_consecutive_auto_reply=10, + is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"), + code_execution_config={ + "work_dir": "/tmp/autogen_work", + "use_docker": False, + }, + llm_config=llm_config, + system_message="Reply TERMINATE if the task has been solved at full satisfaction.", + ) + + def call_autogen_agent(state: MessagesState): + """Node function that calls the AutoGen agent""" + messages = convert_to_openai_messages(state["messages"]) + last_message = messages[-1] + carryover = messages[:-1] if len(messages) > 1 else [] + + response = user_proxy.initiate_chat( + autogen_agent, + message=last_message, + carryover=carryover + ) + + final_content = response.chat_history[-1]["content"] + return {"messages": {"role": "assistant", "content": final_content}} + + # Create and compile the graph + def create_graph(): + checkpointer = MemorySaver() + builder = StateGraph(MessagesState) + builder.add_node("autogen", call_autogen_agent) + builder.add_edge(START, "autogen") + return builder.compile(checkpointer=checkpointer) + + # Export the graph for LangGraph Platform + graph = create_graph() + ``` + +=== "requirements.txt" + + ``` + langgraph>=0.1.0 + pyautogen>=0.2.0 + langchain-core>=0.1.0 + langchain-openai>=0.0.5 + ``` + +=== "langgraph.json" + + ```json + { + "dependencies": ["."], + "graphs": { + "autogen_agent": "./agent.py:graph" + }, + "env": ".env" + } + ``` + + +## 5. Deploy to LangGraph Platform + +Deploy the graph with the LangGraph Platform CLI: + +``` +pip install -U langgraph-cli +``` + +``` +langgraph deploy --config langgraph.json +``` diff --git a/docs/docs/how-tos/autogen-langgraph-platform.ipynb b/docs/docs/how-tos/autogen-langgraph-platform.ipynb deleted file mode 100644 index 29bd24ba5..000000000 --- a/docs/docs/how-tos/autogen-langgraph-platform.ipynb +++ /dev/null @@ -1,171 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8381b6e0-29a6-48c5-b451-5d2549351249", - "metadata": {}, - "source": [ - "# How to use LangGraph Platform to deploy CrewAI, AutoGen, and other frameworks\n", - "\n", - "[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) provides infrastructure for deploying agents. This integrates seamlessly with LangGraph, but can also work with other frameworks. The way to make this work is to wrap the agent in a single LangGraph node, and have that be the entire graph.\n", - "\n", - "Doing so will allow you to deploy to LangGraph Platform, and allows you to get a lot of the [benefits](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/). You get horizontally scalable infrastructure, a task queue to handle bursty operations, a persistence layer to power short term memory, and long term memory support.\n", - "\n", - "In this guide we show how to do this with an AutoGen agent, but this method should work for agents defined in other frameworks like CrewAI, LlamaIndex, and others as well." - ] - }, - { - "cell_type": "markdown", - "id": "1113cb16-b538-448c-924c-85731ce96ebd", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "f05993fa-9d03-4f45-bc13-0a8d87260d86", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "%pip install autogen langgraph" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f4e0ca12-1714-4776-a30a-9527e519799b", - "metadata": {}, - "outputs": [], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "\n", - "def _set_env(var: str):\n", - " if not os.environ.get(var):\n", - " os.environ[var] = getpass.getpass(f\"{var}: \")\n", - "\n", - "\n", - "_set_env(\"OPENAI_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "1926bbc3-6b06-41e0-9604-860a2bbf8fa3", - "metadata": {}, - "source": [ - "## Define autogen agent\n", - "\n", - "Here we define our AutoGen agent. From https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_web_info.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4a14dc7-d565-4207-8788-525f85b9fb27", - "metadata": {}, - "outputs": [], - "source": [ - "import autogen\n", - "import os\n", - "\n", - "config_list = [{\"model\": \"gpt-4o\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]\n", - "\n", - "llm_config = {\n", - " \"timeout\": 600,\n", - " \"cache_seed\": 42,\n", - " \"config_list\": config_list,\n", - " \"temperature\": 0,\n", - "}\n", - "\n", - "autogen_agent = autogen.AssistantAgent(\n", - " name=\"assistant\",\n", - " llm_config=llm_config,\n", - ")\n", - "\n", - "user_proxy = autogen.UserProxyAgent(\n", - " name=\"user_proxy\",\n", - " human_input_mode=\"NEVER\",\n", - " max_consecutive_auto_reply=10,\n", - " is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n", - " code_execution_config={\n", - " \"work_dir\": \"web\",\n", - " \"use_docker\": False,\n", - " }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n", - " llm_config=llm_config,\n", - " system_message=\"Reply TERMINATE if the task has been solved at full satisfaction. Otherwise, reply CONTINUE, or the reason why the task is not solved yet.\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "b1170836-f23e-4e4c-ab83-ce791cd7fbd2", - "metadata": {}, - "source": [ - "## Wrap in LangGraph\n", - "\n", - "We now wrap the AutoGen agent in a single LangGraph node, and make that the entire graph.\n", - "The main thing this involves is defining an Input and Output schema for the node, which you would need to do if deploying this manually, so it's no extra work" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7b417c16-ff4e-4d5c-a9a9-0aaeeef6ede5", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.graph import StateGraph, MessagesState\n", - "\n", - "\n", - "def call_autogen_agent(state: MessagesState):\n", - " last_message = state[\"messages\"][-1]\n", - " response = user_proxy.initiate_chat(autogen_agent, message=last_message.content)\n", - " # get the final response from the agent\n", - " content = response.chat_history[-1][\"content\"]\n", - " return {\"messages\": {\"role\": \"assistant\", \"content\": content}}\n", - "\n", - "\n", - "graph = StateGraph(MessagesState)\n", - "graph.add_node(call_autogen_agent)\n", - "graph.set_entry_point(\"call_autogen_agent\")\n", - "graph = graph.compile()" - ] - }, - { - "cell_type": "markdown", - "id": "f6a18377-ac29-478f-a76a-b213f1a3c85d", - "metadata": {}, - "source": [ - "## Deploy with LangGraph Platform\n", - "\n", - "You can now deploy this as you normally would with LangGraph Platform. See [these instructions](https://langchain-ai.github.io/langgraph/concepts/deployment_options/) for more details." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/docs/how-tos/multi_agent.ipynb b/docs/docs/how-tos/multi_agent.ipynb deleted file mode 100644 index 6ee5777de..000000000 --- a/docs/docs/how-tos/multi_agent.ipynb +++ /dev/null @@ -1,657 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "34d3d54e-9a2b-481e-bccd-74aca7a53f9a", - "metadata": {}, - "source": [ - "# Build multi-agent systems" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "3f0b4f70-f14e-4026-82c0-874786789ee8", - "metadata": {}, - "source": [ - "A single agent might struggle if it needs to specialize in multiple domains or manage many tools. To tackle this, you can break your agent into smaller, independent agents and composing them into a [multi-agent system](../../concepts/multi_agent).\n", - "\n", - "In multi-agent systems, agents need to communicate between each other. They do so via [handoffs](#handoffs) — a primitive that describes which agent to hand control to and the payload to send to that agent.\n", - "\n", - "This guide covers the following:\n", - "\n", - "* implementing [handoffs](#handoffs) between agents\n", - "* using handoffs and the prebuilt [agent](../../agents/agents) to [build a custom multi-agent system](#build-a-multi-agent-system)\n", - "\n", - "To get started with building multi-agent systems, check out LangGraph [prebuilt implementations](#prebuilt-implementations) of two of the most popular multi-agent architectures — [supervisor](../../agents/multi-agent#supervisor) and [swarm](../../agents/multi-agent#swarm)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7d43e110-16fc-4899-97f1-015d5b804b87", - "metadata": {}, - "source": [ - "## Handoffs\n", - "\n", - "To set up communication between the agents in a multi-agent system you can use [**handoffs**](../../concepts/multi_agent#handoffs) — a pattern where one agent *hands off* control to another. Handoffs allow you to specify:\n", - "\n", - "- **destination**: target agent to navigate to (e.g., name of the LangGraph node to go to)\n", - "- **payload**: information to pass to that agent (e.g., state update)\n", - "\n", - "### Create handoffs\n", - "\n", - "To implement handoffs, you can return [`Command`](../command) objects from your agent nodes or tools:\n", - "\n", - "```python\n", - "from typing import Annotated\n", - "from langchain_core.tools import tool, InjectedToolCallId\n", - "from langgraph.prebuilt import create_react_agent, InjectedState\n", - "from langgraph.graph import StateGraph, START, MessagesState\n", - "from langgraph.types import Command\n", - "\n", - "def create_handoff_tool(*, agent_name: str, description: str | None = None):\n", - " name = f\"transfer_to_{agent_name}\"\n", - " description = description or f\"Transfer to {agent_name}\"\n", - "\n", - " @tool(name, description=description)\n", - " def handoff_tool(\n", - " # highlight-next-line\n", - " state: Annotated[MessagesState, InjectedState], # (1)!\n", - " # highlight-next-line\n", - " tool_call_id: Annotated[str, InjectedToolCallId],\n", - " ) -> Command:\n", - " tool_message = {\n", - " \"role\": \"tool\",\n", - " \"content\": f\"Successfully transferred to {agent_name}\",\n", - " \"name\": name,\n", - " \"tool_call_id\": tool_call_id,\n", - " }\n", - " return Command( # (2)!\n", - " # highlight-next-line\n", - " goto=agent_name, # (3)!\n", - " # highlight-next-line\n", - " update={\"messages\": state[\"messages\"] + [tool_message]}, # (4)!\n", - " # highlight-next-line\n", - " graph=Command.PARENT, # (5)!\n", - " )\n", - " return handoff_tool\n", - "```\n", - "\n", - "1. Access the [state](../../concepts/low_level#state) of the agent that is calling the handoff tool using the [InjectedState][langgraph.prebuilt.InjectedState] annotation. See [this guide](../tool-calling/#read-state) for more information.\n", - "2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs.\n", - "3. Name of the agent or node to hand off to.\n", - "4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.\n", - "5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.\n", - "\n", - "!!! tip\n", - "\n", - " If you want to use tools that return `Command`, you can either use prebuilt [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent] / [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] components, or implement your own tool-executing node that collects `Command` objects returned by the tools and returns a list of them, e.g.:\n", - " \n", - " ```python\n", - " def call_tools(state):\n", - " ...\n", - " commands = [tools_by_name[tool_call[\"name\"]].invoke(tool_call) for tool_call in tool_calls]\n", - " return commands\n", - " ```\n", - "\n", - "!!! Important\n", - "\n", - " This handoff implementation assumes that:\n", - " \n", - " - each agent receives overall message history (across all agents) in the multi-agent system as its input. If you want more control over agent inputs, see [this section](#control-agent-inputs)\n", - " - each agent outputs its internal messages history to the overall message history of the multi-agent system. If you want more control over **how agent outputs are added**, wrap the agent in a separate node function:\n", - "\n", - " ```python\n", - " def call_hotel_assistant(state):\n", - " # return agent's final response,\n", - " # excluding inner monologue\n", - " response = hotel_assistant.invoke(state)\n", - " # highlight-next-line\n", - " return {\"messages\": response[\"messages\"][-1]}\n", - " ```" - ] - }, - { - "cell_type": "markdown", - "id": "3956f12d-285a-4799-a0a5-db13def58a15", - "metadata": {}, - "source": [ - "### Control agent inputs\n", - "\n", - "You can use the [`Send()`][langgraph.types.Send] primitive to directly send data to the worker agents during the handoff. For example, you can request that the calling agent populate a task description for the next agent:\n", - "\n", - "```python\n", - "\n", - "from typing import Annotated\n", - "from langchain_core.tools import tool, InjectedToolCallId\n", - "from langgraph.prebuilt import InjectedState\n", - "from langgraph.graph import StateGraph, START, MessagesState\n", - "# highlight-next-line\n", - "from langgraph.types import Command, Send\n", - "\n", - "def create_task_description_handoff_tool(\n", - " *, agent_name: str, description: str | None = None\n", - "):\n", - " name = f\"transfer_to_{agent_name}\"\n", - " description = description or f\"Ask {agent_name} for help.\"\n", - "\n", - " @tool(name, description=description)\n", - " def handoff_tool(\n", - " # this is populated by the calling agent\n", - " task_description: Annotated[\n", - " str,\n", - " \"Description of what the next agent should do, including all of the relevant context.\",\n", - " ],\n", - " # these parameters are ignored by the LLM\n", - " state: Annotated[MessagesState, InjectedState],\n", - " ) -> Command:\n", - " task_description_message = {\"role\": \"user\", \"content\": task_description}\n", - " agent_input = {**state, \"messages\": [task_description_message]}\n", - " return Command(\n", - " # highlight-next-line\n", - " goto=[Send(agent_name, agent_input)],\n", - " graph=Command.PARENT,\n", - " )\n", - "\n", - " return handoff_tool\n", - "```\n", - "\n", - "See the multi-agent [supervisor](../tutorials/agent_supervisor.ipynb#4-create-delegation-tasks) tutorial for a full example of using [`Send()`][langgraph.types.Send] in handoffs." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "21511f57-7bf3-4223-9a17-ce9fc84c40ab", - "metadata": {}, - "source": [ - "## Build a multi-agent system\n", - "\n", - "You can use handoffs in any agents built with LangGraph. We recommend using the prebuilt [agent](../../agents/overview) or [`ToolNode`](../tool-calling#use-prebuilt-toolnode), as they natively support handoffs tools returning `Command`. Below is an example of how you can implement a multi-agent system for booking travel using handoffs:\n", - "\n", - "```python\n", - "from langgraph.prebuilt import create_react_agent\n", - "from langgraph.graph import StateGraph, START, MessagesState\n", - "\n", - "def create_handoff_tool(*, agent_name: str, description: str | None = None):\n", - " # same implementation as above\n", - " ...\n", - " return Command(...)\n", - "\n", - "# Handoffs\n", - "transfer_to_hotel_assistant = create_handoff_tool(agent_name=\"hotel_assistant\")\n", - "transfer_to_flight_assistant = create_handoff_tool(agent_name=\"flight_assistant\")\n", - "\n", - "# Define agents\n", - "flight_assistant = create_react_agent(\n", - " model=\"anthropic:claude-3-5-sonnet-latest\",\n", - " # highlight-next-line\n", - " tools=[..., transfer_to_hotel_assistant],\n", - " # highlight-next-line\n", - " name=\"flight_assistant\"\n", - ")\n", - "hotel_assistant = create_react_agent(\n", - " model=\"anthropic:claude-3-5-sonnet-latest\",\n", - " # highlight-next-line\n", - " tools=[..., transfer_to_flight_assistant],\n", - " # highlight-next-line\n", - " name=\"hotel_assistant\"\n", - ")\n", - "\n", - "# Define multi-agent graph\n", - "multi_agent_graph = (\n", - " StateGraph(MessagesState)\n", - " # highlight-next-line\n", - " .add_node(flight_assistant)\n", - " # highlight-next-line\n", - " .add_node(hotel_assistant)\n", - " .add_edge(START, \"flight_assistant\")\n", - " .compile()\n", - ")\n", - "```\n", - "\n", - "??? example \"Full example: Multi-agent system for booking travel\"\n", - "\n", - " ```python\n", - " from typing import Annotated\n", - " from langchain_core.messages import convert_to_messages\n", - " from langchain_core.tools import tool, InjectedToolCallId\n", - " from langgraph.prebuilt import create_react_agent, InjectedState\n", - " from langgraph.graph import StateGraph, START, MessagesState\n", - " from langgraph.types import Command\n", - " \n", - " # We'll use `pretty_print_messages` helper to render the streamed agent outputs nicely\n", - " \n", - " def pretty_print_message(message, indent=False):\n", - " pretty_message = message.pretty_repr(html=True)\n", - " if not indent:\n", - " print(pretty_message)\n", - " return\n", - " \n", - " indented = \"\\n\".join(\"\\t\" + c for c in pretty_message.split(\"\\n\"))\n", - " print(indented)\n", - " \n", - " \n", - " def pretty_print_messages(update, last_message=False):\n", - " is_subgraph = False\n", - " if isinstance(update, tuple):\n", - " ns, update = update\n", - " # skip parent graph updates in the printouts\n", - " if len(ns) == 0:\n", - " return\n", - " \n", - " graph_id = ns[-1].split(\":\")[0]\n", - " print(f\"Update from subgraph {graph_id}:\")\n", - " print(\"\\n\")\n", - " is_subgraph = True\n", - " \n", - " for node_name, node_update in update.items():\n", - " update_label = f\"Update from node {node_name}:\"\n", - " if is_subgraph:\n", - " update_label = \"\\t\" + update_label\n", - " \n", - " print(update_label)\n", - " print(\"\\n\")\n", - " \n", - " messages = convert_to_messages(node_update[\"messages\"])\n", - " if last_message:\n", - " messages = messages[-1:]\n", - " \n", - " for m in messages:\n", - " pretty_print_message(m, indent=is_subgraph)\n", - " print(\"\\n\")\n", - "\n", - "\n", - " def create_handoff_tool(*, agent_name: str, description: str | None = None):\n", - " name = f\"transfer_to_{agent_name}\"\n", - " description = description or f\"Transfer to {agent_name}\"\n", - " \n", - " @tool(name, description=description)\n", - " def handoff_tool(\n", - " # highlight-next-line\n", - " state: Annotated[MessagesState, InjectedState], # (1)!\n", - " # highlight-next-line\n", - " tool_call_id: Annotated[str, InjectedToolCallId],\n", - " ) -> Command:\n", - " tool_message = {\n", - " \"role\": \"tool\",\n", - " \"content\": f\"Successfully transferred to {agent_name}\",\n", - " \"name\": name,\n", - " \"tool_call_id\": tool_call_id,\n", - " }\n", - " return Command( # (2)!\n", - " # highlight-next-line\n", - " goto=agent_name, # (3)!\n", - " # highlight-next-line\n", - " update={\"messages\": state[\"messages\"] + [tool_message]}, # (4)!\n", - " # highlight-next-line\n", - " graph=Command.PARENT, # (5)!\n", - " )\n", - " return handoff_tool\n", - " \n", - " # Handoffs\n", - " transfer_to_hotel_assistant = create_handoff_tool(\n", - " agent_name=\"hotel_assistant\",\n", - " description=\"Transfer user to the hotel-booking assistant.\",\n", - " )\n", - " transfer_to_flight_assistant = create_handoff_tool(\n", - " agent_name=\"flight_assistant\",\n", - " description=\"Transfer user to the flight-booking assistant.\",\n", - " )\n", - " \n", - " # Simple agent tools\n", - " def book_hotel(hotel_name: str):\n", - " \"\"\"Book a hotel\"\"\"\n", - " return f\"Successfully booked a stay at {hotel_name}.\"\n", - " \n", - " def book_flight(from_airport: str, to_airport: str):\n", - " \"\"\"Book a flight\"\"\"\n", - " return f\"Successfully booked a flight from {from_airport} to {to_airport}.\"\n", - " \n", - " # Define agents\n", - " flight_assistant = create_react_agent(\n", - " model=\"anthropic:claude-3-5-sonnet-latest\",\n", - " # highlight-next-line\n", - " tools=[book_flight, transfer_to_hotel_assistant],\n", - " prompt=\"You are a flight booking assistant\",\n", - " # highlight-next-line\n", - " name=\"flight_assistant\"\n", - " )\n", - " hotel_assistant = create_react_agent(\n", - " model=\"anthropic:claude-3-5-sonnet-latest\",\n", - " # highlight-next-line\n", - " tools=[book_hotel, transfer_to_flight_assistant],\n", - " prompt=\"You are a hotel booking assistant\",\n", - " # highlight-next-line\n", - " name=\"hotel_assistant\"\n", - " )\n", - " \n", - " # Define multi-agent graph\n", - " multi_agent_graph = (\n", - " StateGraph(MessagesState)\n", - " .add_node(flight_assistant)\n", - " .add_node(hotel_assistant)\n", - " .add_edge(START, \"flight_assistant\")\n", - " .compile()\n", - " )\n", - " \n", - " # Run the multi-agent graph\n", - " for chunk in multi_agent_graph.stream(\n", - " {\n", - " \"messages\": [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"book a flight from BOS to JFK and a stay at McKittrick Hotel\"\n", - " }\n", - " ]\n", - " },\n", - " # highlight-next-line\n", - " subgraphs=True\n", - " ):\n", - " pretty_print_messages(chunk)\n", - " ```\n", - "\n", - " 1. Access agent's state\n", - " 2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs.\n", - " 3. Name of the agent or node to hand off to.\n", - " 4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.\n", - " 5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph." - ] - }, - { - "cell_type": "markdown", - "id": "e8314da4-9971-429b-9e70-58b40795de74", - "metadata": {}, - "source": [ - "## Multi-turn conversation\n", - "\n", - "Users might want to engage in a *multi-turn conversation* with one or more agents. To build a system that can handle this, you can create a node that uses an [`interrupt`][langgraph.types.interrupt] to collect user input and routes back to the **active** agent.\n", - "\n", - "The agents can then be implemented as nodes in a graph that executes agent steps and determines the next action:\n", - "\n", - "1. **Wait for user input** to continue the conversation, or \n", - "2. **Route to another agent** (or back to itself, such as in a loop) via a [handoff](#handoffs)\n", - "\n", - "```python\n", - "def human(state) -> Command[Literal[\"agent\", \"another_agent\"]]:\n", - " \"\"\"A node for collecting user input.\"\"\"\n", - " user_input = interrupt(value=\"Ready for user input.\")\n", - "\n", - " # Determine the active agent.\n", - " active_agent = ...\n", - "\n", - " ...\n", - " return Command(\n", - " update={\n", - " \"messages\": [{\n", - " \"role\": \"human\",\n", - " \"content\": user_input,\n", - " }]\n", - " },\n", - " goto=active_agent\n", - " )\n", - "\n", - "def agent(state) -> Command[Literal[\"agent\", \"another_agent\", \"human\"]]:\n", - " # The condition for routing/halting can be anything, e.g. LLM tool call / structured output, etc.\n", - " goto = get_next_agent(...) # 'agent' / 'another_agent'\n", - " if goto:\n", - " return Command(goto=goto, update={\"my_state_key\": \"my_state_value\"})\n", - " else:\n", - " return Command(goto=\"human\") # Go to human node\n", - "```\n", - "\n", - "??? example \"Full example: multi-agent system for travel recommendations\"\n", - "\n", - " In this example, we will build a team of travel assistant agents that can communicate with each other via handoffs.\n", - " \n", - " We will create 2 agents:\n", - " \n", - " * travel_advisor: can help with travel destination recommendations. Can ask hotel_advisor for help.\n", - " * hotel_advisor: can help with hotel recommendations. Can ask travel_advisor for help.\n", - "\n", - " ```python\n", - " from langchain_anthropic import ChatAnthropic\n", - " from langgraph.graph import MessagesState, StateGraph, START\n", - " from langgraph.prebuilt import create_react_agent, InjectedState\n", - " from langgraph.types import Command, interrupt\n", - " from langgraph.checkpoint.memory import MemorySaver\n", - " \n", - " \n", - " model = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")\n", - "\n", - " class MultiAgentState(MessagesState):\n", - " last_active_agent: str\n", - " \n", - " \n", - " # Define travel advisor tools and ReAct agent\n", - " travel_advisor_tools = [\n", - " get_travel_recommendations,\n", - " make_handoff_tool(agent_name=\"hotel_advisor\"),\n", - " ]\n", - " travel_advisor = create_react_agent(\n", - " model,\n", - " travel_advisor_tools,\n", - " prompt=(\n", - " \"You are a general travel expert that can recommend travel destinations (e.g. countries, cities, etc). \"\n", - " \"If you need hotel recommendations, ask 'hotel_advisor' for help. \"\n", - " \"You MUST include human-readable response before transferring to another agent.\"\n", - " ),\n", - " )\n", - " \n", - " \n", - " def call_travel_advisor(\n", - " state: MultiAgentState,\n", - " ) -> Command[Literal[\"hotel_advisor\", \"human\"]]:\n", - " # You can also add additional logic like changing the input to the agent / output from the agent, etc.\n", - " # NOTE: we're invoking the ReAct agent with the full history of messages in the state\n", - " response = travel_advisor.invoke(state)\n", - " update = {**response, \"last_active_agent\": \"travel_advisor\"}\n", - " return Command(update=update, goto=\"human\")\n", - " \n", - " \n", - " # Define hotel advisor tools and ReAct agent\n", - " hotel_advisor_tools = [\n", - " get_hotel_recommendations,\n", - " make_handoff_tool(agent_name=\"travel_advisor\"),\n", - " ]\n", - " hotel_advisor = create_react_agent(\n", - " model,\n", - " hotel_advisor_tools,\n", - " prompt=(\n", - " \"You are a hotel expert that can provide hotel recommendations for a given destination. \"\n", - " \"If you need help picking travel destinations, ask 'travel_advisor' for help.\"\n", - " \"You MUST include human-readable response before transferring to another agent.\"\n", - " ),\n", - " )\n", - " \n", - " \n", - " def call_hotel_advisor(\n", - " state: MultiAgentState,\n", - " ) -> Command[Literal[\"travel_advisor\", \"human\"]]:\n", - " response = hotel_advisor.invoke(state)\n", - " update = {**response, \"last_active_agent\": \"hotel_advisor\"}\n", - " return Command(update=update, goto=\"human\")\n", - " \n", - " \n", - " def human_node(\n", - " state: MultiAgentState, config\n", - " ) -> Command[Literal[\"hotel_advisor\", \"travel_advisor\", \"human\"]]:\n", - " \"\"\"A node for collecting user input.\"\"\"\n", - " \n", - " user_input = interrupt(value=\"Ready for user input.\")\n", - " active_agent = state[\"last_active_agent\"]\n", - " \n", - " return Command(\n", - " update={\n", - " \"messages\": [\n", - " {\n", - " \"role\": \"human\",\n", - " \"content\": user_input,\n", - " }\n", - " ]\n", - " },\n", - " goto=active_agent,\n", - " )\n", - " \n", - " \n", - " builder = StateGraph(MultiAgentState)\n", - " builder.add_node(\"travel_advisor\", call_travel_advisor)\n", - " builder.add_node(\"hotel_advisor\", call_hotel_advisor)\n", - " \n", - " # This adds a node to collect human input, which will route\n", - " # back to the active agent.\n", - " builder.add_node(\"human\", human_node)\n", - " \n", - " # We'll always start with a general travel advisor.\n", - " builder.add_edge(START, \"travel_advisor\")\n", - " \n", - " \n", - " checkpointer = MemorySaver()\n", - " graph = builder.compile(checkpointer=checkpointer)\n", - " ```\n", - " \n", - " Let's test a multi turn conversation with this application.\n", - "\n", - " ```python\n", - " import uuid\n", - " \n", - " thread_config = {\"configurable\": {\"thread_id\": str(uuid.uuid4())}}\n", - " \n", - " inputs = [\n", - " # 1st round of conversation,\n", - " {\n", - " \"messages\": [\n", - " {\"role\": \"user\", \"content\": \"i wanna go somewhere warm in the caribbean\"}\n", - " ]\n", - " },\n", - " # Since we're using `interrupt`, we'll need to resume using the Command primitive.\n", - " # 2nd round of conversation,\n", - " Command(\n", - " resume=\"could you recommend a nice hotel in one of the areas and tell me which area it is.\"\n", - " ),\n", - " # 3rd round of conversation,\n", - " Command(\n", - " resume=\"i like the first one. could you recommend something to do near the hotel?\"\n", - " ),\n", - " ]\n", - " \n", - " for idx, user_input in enumerate(inputs):\n", - " print()\n", - " print(f\"--- Conversation Turn {idx + 1} ---\")\n", - " print()\n", - " print(f\"User: {user_input}\")\n", - " print()\n", - " for update in graph.stream(\n", - " user_input,\n", - " config=thread_config,\n", - " stream_mode=\"updates\",\n", - " ):\n", - " for node_id, value in update.items():\n", - " if isinstance(value, dict) and value.get(\"messages\", []):\n", - " last_message = value[\"messages\"][-1]\n", - " if isinstance(last_message, dict) or last_message.type != \"ai\":\n", - " continue\n", - " print(f\"{node_id}: {last_message.content}\")\n", - " ```\n", - " \n", - " ```\n", - " --- Conversation Turn 1 ---\n", - " \n", - " User: {'messages': [{'role': 'user', 'content': 'i wanna go somewhere warm in the caribbean'}]}\n", - " \n", - " travel_advisor: Based on the recommendations, Aruba would be an excellent choice for your Caribbean getaway! Aruba is known as \"One Happy Island\" and offers:\n", - " - Year-round warm weather with consistent temperatures around 82°F (28°C)\n", - " - Beautiful white sand beaches like Eagle Beach and Palm Beach\n", - " - Clear turquoise waters perfect for swimming and snorkeling\n", - " - Minimal rainfall and location outside the hurricane belt\n", - " - A blend of Caribbean and Dutch culture\n", - " - Great dining options and nightlife\n", - " - Various water sports and activities\n", - " \n", - " Would you like me to get some specific hotel recommendations in Aruba for your stay? I can transfer you to our hotel advisor who can help with accommodations.\n", - " \n", - " --- Conversation Turn 2 ---\n", - " \n", - " User: Command(resume='could you recommend a nice hotel in one of the areas and tell me which area it is.')\n", - " \n", - " hotel_advisor: Based on the recommendations, I can suggest two excellent options:\n", - " \n", - " 1. The Ritz-Carlton, Aruba - Located in Palm Beach\n", - " - This luxury resort is situated in the vibrant Palm Beach area\n", - " - Known for its exceptional service and amenities\n", - " - Perfect if you want to be close to dining, shopping, and entertainment\n", - " - Features multiple restaurants, a casino, and a world-class spa\n", - " - Located on a pristine stretch of Palm Beach\n", - " \n", - " 2. Bucuti & Tara Beach Resort - Located in Eagle Beach\n", - " - An adults-only boutique resort on Eagle Beach\n", - " - Known for being more intimate and peaceful\n", - " - Award-winning for its sustainability practices\n", - " - Perfect for a romantic getaway or peaceful vacation\n", - " - Located on one of the most beautiful beaches in the Caribbean\n", - " \n", - " Would you like more specific information about either of these properties or their locations?\n", - " \n", - " --- Conversation Turn 3 ---\n", - " \n", - " User: Command(resume='i like the first one. could you recommend something to do near the hotel?')\n", - " \n", - " travel_advisor: Near the Ritz-Carlton in Palm Beach, here are some highly recommended activities:\n", - " \n", - " 1. Visit the Palm Beach Plaza Mall - Just a short walk from the hotel, featuring shopping, dining, and entertainment\n", - " 2. Try your luck at the Stellaris Casino - It's right in the Ritz-Carlton\n", - " 3. Take a sunset sailing cruise - Many depart from the nearby pier\n", - " 4. Visit the California Lighthouse - A scenic landmark just north of Palm Beach\n", - " 5. Enjoy water sports at Palm Beach:\n", - " - Jet skiing\n", - " - Parasailing\n", - " - Snorkeling\n", - " - Stand-up paddleboarding\n", - " \n", - " Would you like more specific information about any of these activities or would you like to know about other options in the area?\n", - " ```" - ] - }, - { - "cell_type": "markdown", - "id": "04d18c63-a0eb-45ac-86dc-0cc5bd683973", - "metadata": {}, - "source": [ - "## Prebuilt implementations" - ] - }, - { - "cell_type": "markdown", - "id": "e0e4ce57-f8de-4f37-836e-c1e1a02dd7b7", - "metadata": {}, - "source": [ - "LangGraph comes with prebuilt implementations of two of the most popular multi-agent architectures:\n", - "\n", - "- [supervisor](../../agents/multi-agent#supervisor) — individual agents are coordinated by a central supervisor agent. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements. You can use [`langgraph-supervisor`](https://github.com/langchain-ai/langgraph-supervisor-py) library to create a supervisor multi-agent systems.\n", - "- [swarm](../../agents/multi-agent#supervisor) — agents dynamically hand off control to one another based on their specializations. The system remembers which agent was last active, ensuring that on subsequent interactions, the conversation resumes with that agent. You can use [`langgraph-swarm`](https://github.com/langchain-ai/langgraph-swarm-py) library to create a swarm multi-agent systems." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/docs/how-tos/multi_agent.md b/docs/docs/how-tos/multi_agent.md index 5aead8b95..fdd781d4f 100644 --- a/docs/docs/how-tos/multi_agent.md +++ b/docs/docs/how-tos/multi_agent.md @@ -130,7 +130,7 @@ def create_task_description_handoff_tool( return handoff_tool ``` -See the multi-agent [supervisor](../tutorials/multi_agent/agent_supervisor.ipynb#4-create-delegation-tasks) example for a full example of using [`Send()`][langgraph.types.Send] in handoffs. +See the multi-agent [supervisor](../tutorials/multi_agent/agent_supervisor.md#4-create-delegation-tasks) example for a full example of using [`Send()`][langgraph.types.Send] in handoffs. ## Build a multi-agent system diff --git a/docs/docs/how-tos/run-id-langsmith.ipynb b/docs/docs/how-tos/run-id-langsmith.ipynb deleted file mode 100644 index 550c8ae64..000000000 --- a/docs/docs/how-tos/run-id-langsmith.ipynb +++ /dev/null @@ -1,274 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to pass custom run ID or set tags and metadata for graph runs in LangSmith\n", - "\n", - "

\n", - "

Prerequisites

\n", - "

\n", - " This guide assumes familiarity with the following:\n", - "

\n", - "

\n", - "
\n", - "\n", - "Debugging graph runs can sometimes be difficult to do in an IDE or terminal. [LangSmith](https://docs.smith.langchain.com) lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read the [LangSmith documentation](https://docs.smith.langchain.com) for more information on how to get started.\n", - "\n", - "To make it easier to identify and analyzed traces generated during graph invocation, you can set additional configuration at run time (see [RunnableConfig](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig)):\n", - "\n", - "| **Field** | **Type** | **Description** |\n", - "|-------------|---------------------|--------------------------------------------------------------------------------------------------------------------|\n", - "| run_name | `str` | Name for the tracer run for this call. Defaults to the name of the class. |\n", - "| run_id | `UUID` | Unique identifier for the tracer run for this call. If not provided, a new UUID will be generated. |\n", - "| tags | `List[str]` | Tags for this call and any sub-calls (e.g., a Chain calling an LLM). You can use these to filter calls. |\n", - "| metadata | `Dict[str, Any]` | Metadata for this call and any sub-calls (e.g., a Chain calling an LLM). Keys should be strings, values should be JSON-serializable. |\n", - "\n", - "LangGraph graphs implement the [LangChain Runnable Interface](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) and accept a second argument (`RunnableConfig`) in methods like `invoke`, `ainvoke`, `stream` etc.\n", - "\n", - "The LangSmith platform will allow you to search and filter traces based on `run_name`, `run_id`, `tags` and `metadata`.\n", - "\n", - "\n", - "## TLDR\n", - "\n", - "```python\n", - "import uuid\n", - "# Generate a random UUID -- it must be a UUID\n", - "config = {\"run_id\": uuid.uuid4()}, \"tags\": [\"my_tag1\"], \"metadata\": {\"a\": 5}}\n", - "# Works with all standard Runnable methods \n", - "# like invoke, batch, ainvoke, astream_events etc\n", - "graph.stream(inputs, config, stream_mode=\"values\")\n", - "```\n", - "\n", - "The rest of the how to guide will show a full agent.\n", - "\n", - "## Setup\n", - "\n", - "First, let's install the required packages and set our API keys" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install --quiet -U langgraph langchain_openai" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "\n", - "def _set_env(var: str):\n", - " if not os.environ.get(var):\n", - " os.environ[var] = getpass.getpass(f\"{var}: \")\n", - "\n", - "\n", - "_set_env(\"OPENAI_API_KEY\")\n", - "_set_env(\"LANGSMITH_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "

Set up LangSmith for LangGraph development

\n", - "

\n", - " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started here. \n", - "

\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define the graph\n", - "\n", - "For this example we will use the [prebuilt ReAct agent](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/)." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_openai import ChatOpenAI\n", - "from typing import Literal\n", - "from langgraph.prebuilt import create_react_agent\n", - "from langchain_core.tools import tool\n", - "\n", - "# First we initialize the model we want to use.\n", - "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n", - "\n", - "\n", - "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n", - "@tool\n", - "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n", - " \"\"\"Use this to get weather information.\"\"\"\n", - " if city == \"nyc\":\n", - " return \"It might be cloudy in nyc\"\n", - " elif city == \"sf\":\n", - " return \"It's always sunny in sf\"\n", - " else:\n", - " raise AssertionError(\"Unknown city\")\n", - "\n", - "\n", - "tools = [get_weather]\n", - "\n", - "\n", - "# Define the graph\n", - "graph = create_react_agent(model, tools=tools)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run your graph\n", - "\n", - "Now that we've defined our graph let's run it once and view the trace in LangSmith. In order for our trace to be easily accessible in LangSmith, we will pass in a custom `run_id` in the config.\n", - "\n", - "This assumes that you have set your `LANGSMITH_API_KEY` environment variable.\n", - "\n", - "Note that you can also configure what project to trace to by setting the `LANGCHAIN_PROJECT` environment variable, by default runs will be traced to the `default` project." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "what is the weather in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " get_weather (call_9ZudXyMAdlUjptq9oMGtQo8o)\n", - " Call ID: call_9ZudXyMAdlUjptq9oMGtQo8o\n", - " Args:\n", - " city: sf\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: get_weather\n", - "\n", - "It's always sunny in sf\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "The weather in San Francisco is currently sunny.\n" - ] - } - ], - "source": [ - "import uuid\n", - "\n", - "\n", - "def print_stream(stream):\n", - " for s in stream:\n", - " message = s[\"messages\"][-1]\n", - " if isinstance(message, tuple):\n", - " print(message)\n", - " else:\n", - " message.pretty_print()\n", - "\n", - "\n", - "inputs = {\"messages\": [(\"user\", \"what is the weather in sf\")]}\n", - "\n", - "config = {\"run_name\": \"agent_007\", \"tags\": [\"cats are awesome\"]}\n", - "\n", - "print_stream(graph.stream(inputs, config, stream_mode=\"values\"))" - ] - }, - { - "attachments": { - "d38d1f2b-0f4c-4707-b531-a3c749de987f.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View the trace in LangSmith\n", - "\n", - "Now that we've ran our graph, let's head over to LangSmith and view our trace. First click into the project that you traced to (in our case the default project). You should see a run with the custom run name \"agent_007\".\n", - "\n", - "![image.png](attachment:d38d1f2b-0f4c-4707-b531-a3c749de987f.png)" - ] - }, - { - "attachments": { - "410e0089-2ab8-46bb-a61a-827187fd46b3.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition, you will be able to filter traces after the fact using the tags or metadata provided. For example,\n", - "\n", - "![image.png](attachment:410e0089-2ab8-46bb-a61a-827187fd46b3.png)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/docs/how-tos/run-id-langsmith.md b/docs/docs/how-tos/run-id-langsmith.md new file mode 100644 index 000000000..0cd87e864 --- /dev/null +++ b/docs/docs/how-tos/run-id-langsmith.md @@ -0,0 +1,155 @@ +# How to pass custom run ID or set tags and metadata for graph runs in LangSmith + +!!! tip "Prerequisites" + This guide assumes familiarity with the following: + + - [LangSmith Documentation](https://docs.smith.langchain.com) + - [LangSmith Platform](https://smith.langchain.com) + - [RunnableConfig](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig) + - [Add metadata and tags to traces](https://docs.smith.langchain.com/how_to_guides/tracing/trace_with_langchain#add-metadata-and-tags-to-traces) + - [Customize run name](https://docs.smith.langchain.com/how_to_guides/tracing/trace_with_langchain#customize-run-name) + +Debugging graph runs can sometimes be difficult to do in an IDE or terminal. [LangSmith](https://docs.smith.langchain.com) lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read the [LangSmith documentation](https://docs.smith.langchain.com) for more information on how to get started. + +To make it easier to identify and analyzed traces generated during graph invocation, you can set additional configuration at run time (see [RunnableConfig](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig)): + +| **Field** | **Type** | **Description** | +|-------------|---------------------|--------------------------------------------------------------------------------------------------------------------| +| run_name | `str` | Name for the tracer run for this call. Defaults to the name of the class. | +| run_id | `UUID` | Unique identifier for the tracer run for this call. If not provided, a new UUID will be generated. | +| tags | `List[str]` | Tags for this call and any sub-calls (e.g., a Chain calling an LLM). You can use these to filter calls. | +| metadata | `Dict[str, Any]` | Metadata for this call and any sub-calls (e.g., a Chain calling an LLM). Keys should be strings, values should be JSON-serializable. | + +LangGraph graphs implement the [LangChain Runnable Interface](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) and accept a second argument (`RunnableConfig`) in methods like `invoke`, `ainvoke`, `stream` etc. + +The LangSmith platform will allow you to search and filter traces based on `run_name`, `run_id`, `tags` and `metadata`. + +## TLDR + +```python +import uuid +# Generate a random UUID -- it must be a UUID +config = {"run_id": uuid.uuid4()}, "tags": ["my_tag1"], "metadata": {"a": 5}} +# Works with all standard Runnable methods +# like invoke, batch, ainvoke, astream_events etc +graph.stream(inputs, config, stream_mode="values") +``` + +The rest of the how to guide will show a full agent. + +## Setup + +First, let's install the required packages and set our API keys + +```python +%%capture --no-stderr +%pip install --quiet -U langgraph langchain_openai +``` + +```python +import getpass +import os + + +def _set_env(var: str): + if not os.environ.get(var): + os.environ[var] = getpass.getpass(f"{var}: ") + + +_set_env("OPENAI_API_KEY") +_set_env("LANGSMITH_API_KEY") +``` + +!!! tip + Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. [LangSmith](https://docs.smith.langchain.com) lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started [here](https://docs.smith.langchain.com). + +## Define the graph + +For this example we will use the [prebuilt ReAct agent](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/). + +```python +from langchain_openai import ChatOpenAI +from typing import Literal +from langgraph.prebuilt import create_react_agent +from langchain_core.tools import tool + +# First we initialize the model we want to use. +model = ChatOpenAI(model="gpt-4o", temperature=0) + + +# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF) +@tool +def get_weather(city: Literal["nyc", "sf"]): + """Use this to get weather information.""" + if city == "nyc": + return "It might be cloudy in nyc" + elif city == "sf": + return "It's always sunny in sf" + else: + raise AssertionError("Unknown city") + + +tools = [get_weather] + + +# Define the graph +graph = create_react_agent(model, tools=tools) +``` + +## Run your graph + +Now that we've defined our graph let's run it once and view the trace in LangSmith. In order for our trace to be easily accessible in LangSmith, we will pass in a custom `run_id` in the config. + +This assumes that you have set your `LANGSMITH_API_KEY` environment variable. + +Note that you can also configure what project to trace to by setting the `LANGCHAIN_PROJECT` environment variable, by default runs will be traced to the `default` project. + +```python +import uuid + + +def print_stream(stream): + for s in stream: + message = s["messages"][-1] + if isinstance(message, tuple): + print(message) + else: + message.pretty_print() + + +inputs = {"messages": [("user", "what is the weather in sf")]} + +config = {"run_name": "agent_007", "tags": ["cats are awesome"]} + +print_stream(graph.stream(inputs, config, stream_mode="values")) +``` + +**Output:** +``` +================================ Human Message ================================== + +what is the weather in sf +================================== Ai Message =================================== +Tool Calls: + get_weather (call_9ZudXyMAdlUjptq9oMGtQo8o) + Call ID: call_9ZudXyMAdlUjptq9oMGtQo8o + Args: + city: sf +================================= Tool Message ================================== +Name: get_weather + +It's always sunny in sf +================================== Ai Message =================================== + +The weather in San Francisco is currently sunny. +``` + +## View the trace in LangSmith + +Now that we've ran our graph, let's head over to LangSmith and view our trace. First click into the project that you traced to (in our case the default project). You should see a run with the custom run name "agent_007". + +![LangSmith Trace View](assets/d38d1f2b-0f4c-4707-b531-a3c749de987f.png) + +In addition, you will be able to filter traces after the fact using the tags or metadata provided. For example, + +![LangSmith Filter View](assets/410e0089-2ab8-46bb-a61a-827187fd46b3.png) \ No newline at end of file diff --git a/docs/docs/tutorials/multi_agent/agent_supervisor.ipynb b/docs/docs/tutorials/multi_agent/agent_supervisor.ipynb deleted file mode 100644 index 2303872ef..000000000 --- a/docs/docs/tutorials/multi_agent/agent_supervisor.ipynb +++ /dev/null @@ -1,1403 +0,0 @@ -{ - "cells": [ - { - "attachments": { - "8ee0a8ce-f0a8-4019-b5bf-b20933e40956.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "a3e3ebc4-57af-4fe4-bdd3-36aff67bf276", - "metadata": {}, - "source": [ - "# Multi-agent supervisor\n", - "\n", - "[**Supervisor**](../../../concepts/multi_agent#supervisor) is a multi-agent architecture where **specialized** agents are coordinated by a central **supervisor agent**. The supervisor agent controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements.\n", - "\n", - "In this tutorial, you will build a supervisor system with two agents — a research and a math expert. By the end of the tutorial you will:\n", - "\n", - "1. Build specialized research and math agents\n", - "2. Build a supervisor for orchestrating them with the prebuilt [`langgraph-supervisor`](https://langchain-ai.github.io/langgraph/agents/multi-agent/#supervisor)\n", - "3. Build a supervisor from scratch\n", - "4. Implement advanced task delegation\n", - "\n", - "![diagram](attachment:8ee0a8ce-f0a8-4019-b5bf-b20933e40956.png)\n", - "\n", - "## Setup\n", - "\n", - "First, let's install required packages and set our API keys" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0d30b6f7-3bec-4d9f-af50-43dfdc81ae6c", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install -U langgraph langgraph-supervisor langchain-tavily \"langchain[openai]\"" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c84adef6-51b3-46c5-8490-c2a1ad967769", - "metadata": {}, - "outputs": [], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "\n", - "def _set_if_undefined(var: str):\n", - " if not os.environ.get(var):\n", - " os.environ[var] = getpass.getpass(f\"Please provide your {var}\")\n", - "\n", - "\n", - "_set_if_undefined(\"OPENAI_API_KEY\")\n", - "_set_if_undefined(\"TAVILY_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "be85e3ad", - "metadata": {}, - "source": [ - "
\n", - "

Set up LangSmith for LangGraph development

\n", - "

\n", - " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started here. \n", - "

\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "349a5673-962e-4de9-ac19-70ee426ef433", - "metadata": {}, - "source": [ - "## 1. Create worker agents" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ad822cf5-56d4-47f7-ad64-d9fd3f57551e", - "metadata": {}, - "source": [ - "First, let's create our specialized worker agents — research agent and math agent:\n", - "\n", - "* Research agent will have access to a web search tool using [Tavily API](https://tavily.com/)\n", - "* Math agent will have access to simple math tools (`add`, `multiply`, `divide`)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "5e34989a-8989-432e-b6aa-8de91ab83372", - "metadata": {}, - "source": [ - "### Research agent\n", - "\n", - "For web search, we will use `TavilySearch` tool from `langchain-tavily`:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0fdc7345-2967-484b-881a-114dfac3f534", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Find events, attractions, deals, and more at nyctourism.com Skip Main Navigation Menu The Official Website of the City of New York Text Size Powered by Translate SearchSearch Primary Navigation The official website of NYC Home NYC Resources NYC311 Office of the Mayor Events Connect Jobs Search Office of the Mayor | Mayor's Bio | City of New York Secondary Navigation MayorBiographyNewsOfficials Eric L. Adams 110th Mayor of New York City Mayor Eric Adams has served the people of New York City as an NYPD officer, State Senator, Brooklyn Borough President, and now as the 110th Mayor of the City of New York. Mayor Eric Adams has served the people of New York City as an NYPD officer, State Senator, Brooklyn Borough President, and now as the 110th Mayor of the City of New York. He gave voice to a diverse coalition of working families in all five boroughs and is leading the fight to bring back New York City’s economy, reduce inequality, improve public safety, and build a stronger, healthier city that delivers for all New Yorkers. As the representative of one of the nation’s largest counties, Eric fought tirelessly to grow the local economy, invest in schools, reduce inequality, improve public safety, and advocate for smart policies and better government that delivers for all New Yorkers.\n" - ] - } - ], - "source": [ - "from langchain_tavily import TavilySearch\n", - "\n", - "web_search = TavilySearch(max_results=3)\n", - "web_search_results = web_search.invoke(\"who is the mayor of NYC?\")\n", - "\n", - "print(web_search_results[\"results\"][0][\"content\"])" - ] - }, - { - "cell_type": "markdown", - "id": "cc899eb6-b08e-41b8-b92b-75908c44e8b0", - "metadata": {}, - "source": [ - "To create individual worker agents, we will use LangGraph's prebuilt [agent](../../../agents/agents#basic-configuration)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "15e179e4-1a49-4c76-9235-0ca989eb0d2d", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.prebuilt import create_react_agent\n", - "\n", - "research_agent = create_react_agent(\n", - " model=\"openai:gpt-4.1\",\n", - " tools=[web_search],\n", - " prompt=(\n", - " \"You are a research agent.\\n\\n\"\n", - " \"INSTRUCTIONS:\\n\"\n", - " \"- Assist ONLY with research-related tasks, DO NOT do any math\\n\"\n", - " \"- After you're done with your tasks, respond to the supervisor directly\\n\"\n", - " \"- Respond ONLY with the results of your work, do NOT include ANY other text.\"\n", - " ),\n", - " name=\"research_agent\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "023ae2f2-517b-4611-a0ca-506b8c0b734e", - "metadata": {}, - "source": [ - "Let's [run the agent](../../../agents/run_agents) to verify that it behaves as expected. \n", - "\n", - "\n", - "??? note \"We'll use `pretty_print_messages` helper to render the streamed agent outputs nicely\"\n", - "\n", - " ```python\n", - " from langchain_core.messages import convert_to_messages\n", - " \n", - " \n", - " def pretty_print_message(message, indent=False):\n", - " pretty_message = message.pretty_repr(html=True)\n", - " if not indent:\n", - " print(pretty_message)\n", - " return\n", - " \n", - " indented = \"\\n\".join(\"\\t\" + c for c in pretty_message.split(\"\\n\"))\n", - " print(indented)\n", - " \n", - " \n", - " def pretty_print_messages(update, last_message=False):\n", - " is_subgraph = False\n", - " if isinstance(update, tuple):\n", - " ns, update = update\n", - " # skip parent graph updates in the printouts\n", - " if len(ns) == 0:\n", - " return\n", - " \n", - " graph_id = ns[-1].split(\":\")[0]\n", - " print(f\"Update from subgraph {graph_id}:\")\n", - " print(\"\\n\")\n", - " is_subgraph = True\n", - " \n", - " for node_name, node_update in update.items():\n", - " update_label = f\"Update from node {node_name}:\"\n", - " if is_subgraph:\n", - " update_label = \"\\t\" + update_label\n", - " \n", - " print(update_label)\n", - " print(\"\\n\")\n", - " \n", - " messages = convert_to_messages(node_update[\"messages\"])\n", - " if last_message:\n", - " messages = messages[-1:]\n", - " \n", - " for m in messages:\n", - " pretty_print_message(m, indent=is_subgraph)\n", - " print(\"\\n\")\n", - " ```" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "bfaa81e6", - "metadata": {}, - "outputs": [], - "source": [ - "# hide-cell\n", - "from langchain_core.messages import convert_to_messages\n", - "\n", - "\n", - "def pretty_print_message(message, indent=False):\n", - " pretty_message = message.pretty_repr(html=True)\n", - " if not indent:\n", - " print(pretty_message)\n", - " return\n", - "\n", - " indented = \"\\n\".join(\"\\t\" + c for c in pretty_message.split(\"\\n\"))\n", - " print(indented)\n", - "\n", - "\n", - "def pretty_print_messages(update, last_message=False):\n", - " is_subgraph = False\n", - " if isinstance(update, tuple):\n", - " ns, update = update\n", - " # skip parent graph updates in the printouts\n", - " if len(ns) == 0:\n", - " return\n", - "\n", - " graph_id = ns[-1].split(\":\")[0]\n", - " print(f\"Update from subgraph {graph_id}:\")\n", - " print(\"\\n\")\n", - " is_subgraph = True\n", - "\n", - " for node_name, node_update in update.items():\n", - " update_label = f\"Update from node {node_name}:\"\n", - " if is_subgraph:\n", - " update_label = \"\\t\" + update_label\n", - "\n", - " print(update_label)\n", - " print(\"\\n\")\n", - "\n", - " messages = convert_to_messages(node_update[\"messages\"])\n", - " if last_message:\n", - " messages = messages[-1:]\n", - "\n", - " for m in messages:\n", - " pretty_print_message(m, indent=is_subgraph)\n", - " print(\"\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "71474c5b-6a9b-460e-bf30-77831976aaf9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Update from node agent:\n", - "\n", - "\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: research_agent\n", - "Tool Calls:\n", - " tavily_search (call_U748rQhQXT36sjhbkYLSXQtJ)\n", - " Call ID: call_U748rQhQXT36sjhbkYLSXQtJ\n", - " Args:\n", - " query: current mayor of New York City\n", - " search_depth: basic\n", - "\n", - "\n", - "Update from node tools:\n", - "\n", - "\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: tavily_search\n", - "\n", - "{\"query\": \"current mayor of New York City\", \"follow_up_questions\": null, \"answer\": null, \"images\": [], \"results\": [{\"title\": \"List of mayors of New York City - Wikipedia\", \"url\": \"https://en.wikipedia.org/wiki/List_of_mayors_of_New_York_City\", \"content\": \"The mayor of New York City is the chief executive of the Government of New York City, as stipulated by New York City's charter.The current officeholder, the 110th in the sequence of regular mayors, is Eric Adams, a member of the Democratic Party.. During the Dutch colonial period from 1624 to 1664, New Amsterdam was governed by the Director of New Netherland.\", \"score\": 0.9039154, \"raw_content\": null}, {\"title\": \"Office of the Mayor | Mayor's Bio | City of New York - NYC.gov\", \"url\": \"https://www.nyc.gov/office-of-the-mayor/bio.page\", \"content\": \"Mayor Eric Adams has served the people of New York City as an NYPD officer, State Senator, Brooklyn Borough President, and now as the 110th Mayor of the City of New York. He gave voice to a diverse coalition of working families in all five boroughs and is leading the fight to bring back New York City's economy, reduce inequality, improve\", \"score\": 0.8405867, \"raw_content\": null}, {\"title\": \"Eric Adams - Wikipedia\", \"url\": \"https://en.wikipedia.org/wiki/Eric_Adams\", \"content\": \"Eric Leroy Adams (born September 1, 1960) is an American politician and former police officer who has served as the 110th mayor of New York City since 2022. Adams was an officer in the New York City Transit Police and then the New York City Police Department (NYPD) for more than 20 years, retiring at the rank of captain.He served in the New York State Senate from 2006 to 2013, representing the\", \"score\": 0.77731717, \"raw_content\": null}], \"response_time\": 1.81}\n", - "\n", - "\n", - "Update from node agent:\n", - "\n", - "\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: research_agent\n", - "\n", - "The current mayor of New York City is Eric Adams.\n", - "\n", - "\n" - ] - } - ], - "source": [ - "for chunk in research_agent.stream(\n", - " {\"messages\": [{\"role\": \"user\", \"content\": \"who is the mayor of NYC?\"}]}\n", - "):\n", - " pretty_print_messages(chunk)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "95e76c28-e9c9-42b7-8381-5e2a07f900cb", - "metadata": {}, - "source": [ - "### Math agent\n", - "\n", - "For math agent tools we will use [vanilla Python functions](../../../agents/tools#define-simple-tools):" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "95910bb8-7144-4015-9ea4-d97d0c22db12", - "metadata": {}, - "outputs": [], - "source": [ - "def add(a: float, b: float):\n", - " \"\"\"Add two numbers.\"\"\"\n", - " return a + b\n", - "\n", - "\n", - "def multiply(a: float, b: float):\n", - " \"\"\"Multiply two numbers.\"\"\"\n", - " return a * b\n", - "\n", - "\n", - "def divide(a: float, b: float):\n", - " \"\"\"Divide two numbers.\"\"\"\n", - " return a / b\n", - "\n", - "\n", - "math_agent = create_react_agent(\n", - " model=\"openai:gpt-4.1\",\n", - " tools=[add, multiply, divide],\n", - " prompt=(\n", - " \"You are a math agent.\\n\\n\"\n", - " \"INSTRUCTIONS:\\n\"\n", - " \"- Assist ONLY with math-related tasks\\n\"\n", - " \"- After you're done with your tasks, respond to the supervisor directly\\n\"\n", - " \"- Respond ONLY with the results of your work, do NOT include ANY other text.\"\n", - " ),\n", - " name=\"math_agent\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "0df97bc0-f38b-4893-a135-c16cecbfa759", - "metadata": {}, - "source": [ - "Let's run the math agent:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f803943c-6dd2-4b42-93ba-0210ec276132", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Update from node agent:\n", - "\n", - "\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: math_agent\n", - "Tool Calls:\n", - " add (call_p6OVLDHB4LyCNCxPOZzWR15v)\n", - " Call ID: call_p6OVLDHB4LyCNCxPOZzWR15v\n", - " Args:\n", - " a: 3\n", - " b: 5\n", - "\n", - "\n", - "Update from node tools:\n", - "\n", - "\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: add\n", - "\n", - "8.0\n", - "\n", - "\n", - "Update from node agent:\n", - "\n", - "\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: math_agent\n", - "Tool Calls:\n", - " multiply (call_EoaWHMLFZAX4AkajQCtZvbli)\n", - " Call ID: call_EoaWHMLFZAX4AkajQCtZvbli\n", - " Args:\n", - " a: 8\n", - " b: 7\n", - "\n", - "\n", - "Update from node tools:\n", - "\n", - "\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: multiply\n", - "\n", - "56.0\n", - "\n", - "\n", - "Update from node agent:\n", - "\n", - "\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: math_agent\n", - "\n", - "56\n", - "\n", - "\n" - ] - } - ], - "source": [ - "for chunk in math_agent.stream(\n", - " {\"messages\": [{\"role\": \"user\", \"content\": \"what's (3 + 5) x 7\"}]}\n", - "):\n", - " pretty_print_messages(chunk)" - ] - }, - { - "cell_type": "markdown", - "id": "dceefc4c-95e1-4565-8946-4c113a11416a", - "metadata": {}, - "source": [ - "## 2. Create supervisor with `langgraph-supervisor`" - ] - }, - { - "cell_type": "markdown", - "id": "eb5637a4-8ecb-4c2c-82f5-3674205e37b5", - "metadata": {}, - "source": [ - "To implement out multi-agent system, we will use [`create_supervisor`][langgraph_supervisor.supervisor.create_supervisor] from the prebuilt `langgraph-supervisor` library:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6f32a820-1f3d-4698-831b-46252e2316bc", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph_supervisor import create_supervisor\n", - "from langchain.chat_models import init_chat_model\n", - "\n", - "supervisor = create_supervisor(\n", - " model=init_chat_model(\"openai:gpt-4.1\"),\n", - " agents=[research_agent, math_agent],\n", - " prompt=(\n", - " \"You are a supervisor managing two agents:\\n\"\n", - " \"- a research agent. Assign research-related tasks to this agent\\n\"\n", - " \"- a math agent. Assign math-related tasks to this agent\\n\"\n", - " \"Assign work to one agent at a time, do not call agents in parallel.\\n\"\n", - " \"Do not do any work yourself.\"\n", - " ),\n", - " add_handoff_back_messages=True,\n", - " output_mode=\"full_history\",\n", - ").compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "4c45a3c2-4d9e-4760-87bc-3cc2c007a167", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import display, Image\n", - "\n", - "display(Image(supervisor.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "3a94c3d1-3617-4d68-ac9a-b9bb2c05a604", - "metadata": {}, - "source": [ - "Let's now run it with a query that requires both agents:\n", - "\n", - "* research agent will look up the necessary GDP information\n", - "* math agent will perform division to find the percentage of NY state GDP, as requested" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "89b77171-58d0-4b36-8cb3-b69d69e4bdf9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Update from node supervisor:\n", - "\n", - "\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: transfer_to_research_agent\n", - "\n", - "Successfully transferred to research_agent\n", - "\n", - "\n", - "Update from node research_agent:\n", - "\n", - "\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: transfer_back_to_supervisor\n", - "\n", - "Successfully transferred back to supervisor\n", - "\n", - "\n", - "Update from node supervisor:\n", - "\n", - "\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: transfer_to_math_agent\n", - "\n", - "Successfully transferred to math_agent\n", - "\n", - "\n", - "Update from node math_agent:\n", - "\n", - "\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: transfer_back_to_supervisor\n", - "\n", - "Successfully transferred back to supervisor\n", - "\n", - "\n", - "Update from node supervisor:\n", - "\n", - "\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: supervisor\n", - "\n", - "In 2024, the US GDP was $29.18 trillion and New York State's GDP was $2.297 trillion. New York State accounted for approximately 7.87% of the total US GDP in 2024.\n", - "\n", - "\n" - ] - } - ], - "source": [ - "for chunk in supervisor.stream(\n", - " {\n", - " \"messages\": [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"find US and New York state GDP in 2024. what % of US GDP was New York state?\",\n", - " }\n", - " ]\n", - " },\n", - "):\n", - " pretty_print_messages(chunk, last_message=True)\n", - "\n", - "final_message_history = chunk[\"supervisor\"][\"messages\"]" - ] - }, - { - "cell_type": "markdown", - "id": "599b8d13-70a9-40f3-99b9-5e3d35cc6f56", - "metadata": {}, - "source": [ - "## 3. Create supervisor from scratch" - ] - }, - { - "cell_type": "markdown", - "id": "e23764f8-f818-45f3-a5ee-4cb7b7002336", - "metadata": {}, - "source": [ - "Let's now implement this same multi-agent system from scratch. We will need to:\n", - "\n", - "1. Set up how the supervisor [communicates](#set-up-agent-communication) with individual agents\n", - "2. Create the [supervisor agent](#create-supervisor-agent)\n", - "3. [Combine]() supervisor and worker agents into a single multi-agent graph." - ] - }, - { - "cell_type": "markdown", - "id": "78d32089-0259-4aa3-a473-077ecce342dd", - "metadata": {}, - "source": [ - "### Set up agent communication" - ] - }, - { - "cell_type": "markdown", - "id": "a6c6b436-e95b-476b-b427-7b02d8384cc5", - "metadata": {}, - "source": [ - "We will need to define a way for the supervisor agent to communicate with the worker agents. A common way to implement this in multi-agent architectures is using **handoffs**, where one agent *hands off* control to another. Handoffs allow you to specify:\n", - "\n", - "- **destination**: target agent to transfer to\n", - "- **payload**: information to pass to that agent\n", - "\n", - "We will implement handoffs via **handoff tools** and give these tools to the supervisor agent: when the supervisor calls these tools, it will hand off control to a worker agent, passing the full message history to that agent." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7873a6c3-7de0-46d1-96ce-2dca7c62245a", - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Annotated\n", - "from langchain_core.tools import tool, InjectedToolCallId\n", - "from langgraph.prebuilt import InjectedState\n", - "from langgraph.graph import StateGraph, START, MessagesState\n", - "from langgraph.types import Command\n", - "\n", - "\n", - "def create_handoff_tool(*, agent_name: str, description: str | None = None):\n", - " name = f\"transfer_to_{agent_name}\"\n", - " description = description or f\"Ask {agent_name} for help.\"\n", - "\n", - " @tool(name, description=description)\n", - " def handoff_tool(\n", - " state: Annotated[MessagesState, InjectedState],\n", - " tool_call_id: Annotated[str, InjectedToolCallId],\n", - " ) -> Command:\n", - " tool_message = {\n", - " \"role\": \"tool\",\n", - " \"content\": f\"Successfully transferred to {agent_name}\",\n", - " \"name\": name,\n", - " \"tool_call_id\": tool_call_id,\n", - " }\n", - " # highlight-next-line\n", - " return Command(\n", - " # highlight-next-line\n", - " goto=agent_name, # (1)!\n", - " # highlight-next-line\n", - " update={**state, \"messages\": state[\"messages\"] + [tool_message]}, # (2)!\n", - " # highlight-next-line\n", - " graph=Command.PARENT, # (3)!\n", - " )\n", - "\n", - " return handoff_tool\n", - "\n", - "\n", - "# Handoffs\n", - "assign_to_research_agent = create_handoff_tool(\n", - " agent_name=\"research_agent\",\n", - " description=\"Assign task to a researcher agent.\",\n", - ")\n", - "\n", - "assign_to_math_agent = create_handoff_tool(\n", - " agent_name=\"math_agent\",\n", - " description=\"Assign task to a math agent.\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "3bc810a4-1866-4915-abd9-12655954e673", - "metadata": {}, - "source": [ - "1. Name of the agent or node to hand off to.\n", - "2. Take the agent's messages and add them to the parent's state as part of the handoff. The next agent will see the parent state.\n", - "3. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph." - ] - }, - { - "cell_type": "markdown", - "id": "6a7bd301-c954-4189-87e2-b3bf42d187c1", - "metadata": {}, - "source": [ - "### Create supervisor agent" - ] - }, - { - "cell_type": "markdown", - "id": "ca787ce6-46f9-49c3-8f9f-ddb7ff52cdeb", - "metadata": {}, - "source": [ - "Then, let's create the supervisor agent with the handoff tools we just defined. We will use the prebuilt [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "de5d1ff8-e7a0-4efe-93fe-d304eaef29b6", - "metadata": {}, - "outputs": [], - "source": [ - "supervisor_agent = create_react_agent(\n", - " model=\"openai:gpt-4.1\",\n", - " tools=[assign_to_research_agent, assign_to_math_agent],\n", - " prompt=(\n", - " \"You are a supervisor managing two agents:\\n\"\n", - " \"- a research agent. Assign research-related tasks to this agent\\n\"\n", - " \"- a math agent. Assign math-related tasks to this agent\\n\"\n", - " \"Assign work to one agent at a time, do not call agents in parallel.\\n\"\n", - " \"Do not do any work yourself.\"\n", - " ),\n", - " name=\"supervisor\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "dd4bc4e2-e133-42fd-a47f-6fb63fa51697", - "metadata": {}, - "source": [ - "### Create multi-agent graph" - ] - }, - { - "cell_type": "markdown", - "id": "8e6dcae1-eb91-4a07-aef8-53cbed14a002", - "metadata": {}, - "source": [ - "Putting this all together, let's create a graph for our overall multi-agent system. We will add the supervisor and the individual agents as [subgraph](../../../concepts/low_level#subgraphs) nodes." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "584443af-587a-44f3-a320-f942868191e4", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.graph import END\n", - "\n", - "# Define the multi-agent supervisor graph\n", - "supervisor = (\n", - " StateGraph(MessagesState)\n", - " # NOTE: `destinations` is only needed for visualization and doesn't affect runtime behavior\n", - " .add_node(supervisor_agent, destinations=(\"research_agent\", \"math_agent\", END))\n", - " .add_node(research_agent)\n", - " .add_node(math_agent)\n", - " .add_edge(START, \"supervisor\")\n", - " # always return back to the supervisor\n", - " .add_edge(\"research_agent\", \"supervisor\")\n", - " .add_edge(\"math_agent\", \"supervisor\")\n", - " .compile()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "12576211-a946-46ec-bf81-24215597a1fe", - "metadata": {}, - "source": [ - "Notice that we've added explicit [edges](../../../concepts/low_level#edges) from worker agents back to the supervisor — this means that they are guaranteed to return control back to the supervisor. If you want the agents to respond directly to the user (i.e., turn the system into a router, you can remove these edges)." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "0175fe14-5854-4197-b7e8-559335d0f81b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import display, Image\n", - "\n", - "display(Image(supervisor.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "d36496de-7121-4c49-8cb6-58c943c66628", - "metadata": {}, - "source": [ - "With the multi-agent graph created, let's now run it!" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "56ba78e9-d9c1-457c-a073-d606d5d3e013", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Update from node supervisor:\n", - "\n", - "\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: transfer_to_research_agent\n", - "\n", - "Successfully transferred to research_agent\n", - "\n", - "\n", - "Update from node research_agent:\n", - "\n", - "\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: research_agent\n", - "\n", - "- US GDP in 2024 is projected to be about $28.18 trillion USD (Statista; CBO projection).\n", - "- New York State's nominal GDP for 2024 is estimated at approximately $2.16 trillion USD (various economic reports).\n", - "- New York State's share of US GDP in 2024 is roughly 7.7%.\n", - "\n", - "Sources:\n", - "- https://www.statista.com/statistics/216985/forecast-of-us-gross-domestic-product/\n", - "- https://nyassembly.gov/Reports/WAM/2025economic_revenue/2025_report.pdf?v=1740533306\n", - "\n", - "\n", - "Update from node supervisor:\n", - "\n", - "\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: transfer_to_math_agent\n", - "\n", - "Successfully transferred to math_agent\n", - "\n", - "\n", - "Update from node math_agent:\n", - "\n", - "\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: math_agent\n", - "\n", - "US GDP in 2024: $28.18 trillion\n", - "New York State GDP in 2024: $2.16 trillion\n", - "Percentage of US GDP from New York State: 7.67%\n", - "\n", - "\n", - "Update from node supervisor:\n", - "\n", - "\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: supervisor\n", - "\n", - "Here are your results:\n", - "\n", - "- 2024 US GDP (projected): $28.18 trillion USD\n", - "- 2024 New York State GDP (estimated): $2.16 trillion USD\n", - "- New York State's share of US GDP: approximately 7.7%\n", - "\n", - "If you need the calculation steps or sources, let me know!\n", - "\n", - "\n" - ] - } - ], - "source": [ - "for chunk in supervisor.stream(\n", - " {\n", - " \"messages\": [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"find US and New York state GDP in 2024. what % of US GDP was New York state?\",\n", - " }\n", - " ]\n", - " },\n", - "):\n", - " pretty_print_messages(chunk, last_message=True)\n", - "\n", - "final_message_history = chunk[\"supervisor\"][\"messages\"]" - ] - }, - { - "cell_type": "markdown", - "id": "dac583a8-af2c-402f-801e-15a6bc318e44", - "metadata": {}, - "source": [ - "Let's examine the full resulting message history:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "1eff504c-7437-477e-ab28-936c1238eaca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "find US and New York state GDP in 2024. what % of US GDP was New York state?\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: supervisor\n", - "Tool Calls:\n", - " transfer_to_research_agent (call_KlGgvF5ahlAbjX8d2kHFjsC3)\n", - " Call ID: call_KlGgvF5ahlAbjX8d2kHFjsC3\n", - " Args:\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: transfer_to_research_agent\n", - "\n", - "Successfully transferred to research_agent\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: research_agent\n", - "Tool Calls:\n", - " tavily_search (call_ZOaTVUA6DKrOjWQldLhtrsO2)\n", - " Call ID: call_ZOaTVUA6DKrOjWQldLhtrsO2\n", - " Args:\n", - " query: US GDP 2024 estimate or actual\n", - " search_depth: advanced\n", - " tavily_search (call_QsRAasxW9K03lTlqjuhNLFbZ)\n", - " Call ID: call_QsRAasxW9K03lTlqjuhNLFbZ\n", - " Args:\n", - " query: New York state GDP 2024 estimate or actual\n", - " search_depth: advanced\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: tavily_search\n", - "\n", - "{\"query\": \"US GDP 2024 estimate or actual\", \"follow_up_questions\": null, \"answer\": null, \"images\": [], \"results\": [{\"url\": \"https://www.advisorperspectives.com/dshort/updates/2025/05/29/gdp-gross-domestic-product-q1-2025-second-estimate\", \"title\": \"Q1 GDP Second Estimate: Real GDP at -0.2%, Higher Than Expected\", \"content\": \"> Real gross domestic product (GDP) decreased at an annual rate of 0.2 percent in the first quarter of 2025 (January, February, and March), according to the second estimate released by the U.S. Bureau of Economic Analysis. In the fourth quarter of 2024, real GDP increased 2.4 percent. The decrease in real GDP in the first quarter primarily reflected an increase in imports, which are a subtraction in the calculation of GDP, and a decrease in government spending. These movements were partly [...] by [Harry Mamaysky](https://www.advisorperspectives.com/search?author=Harry%20Mamaysky) of [QuantStreet Capital](https://www.advisorperspectives.com/firm/quantstreet-capital), 10/14/2024\\n\\n#### [The Worst of Allan Roth](https://www.advisorperspectives.com/articles/2024/10/21/worst-of-allan-roth/)\\n\\n by [Allan Roth](https://www.advisorperspectives.com/search?author=Allan%20Roth), 10/21/2024 [...] by [Juan Pablo Spinetto](https://www.advisorperspectives.com/search?author=Juan%20Pablo%20Spinetto) of [Bloomberg News](https://www.advisorperspectives.com/firm/bloomberg-news), 04/05/2025\\n\\n#### [Your Clients Are Clueless About Their Risk Tolerance](https://www.advisorperspectives.com/articles/2024/10/23/clients-clueless-about-risk-tolerance/)\\n\\n by [Dan Solin](https://www.advisorperspectives.com/search?author=Dan%20Solin), 10/23/2024\", \"score\": 0.78650844, \"raw_content\": null}, {\"url\": \"https://www.statista.com/statistics/216985/forecast-of-us-gross-domestic-product/\", \"title\": \"GDP forecast U.S. 2034 - Statista\", \"content\": \"[](https://www.statista.com/statistics/216985/forecast-of-us-gross-domestic-product/#statisticContainer) This graph shows a forecast of the gross domestic product of the United States of America for fiscal years 2024 to 2034. GDP refers to the market value of all final goods and services produced within a country in a given period. According to the CBO, the United States GDP will increase steadily over the next decade from 28.18 trillion U.S. dollars in 2023 to 41.65 trillion U.S. dollars in [...] US Congressional Budget Office. (February 7, 2024). Forecast of the gross domestic product of the United States from fiscal year 2024 to fiscal year 2034 (in billion U.S. dollars) [Graph]. In Statista. Retrieved May 20, 2025, from https://www.statista.com/statistics/216985/forecast-of-us-gross-domestic-product/ [...] US Congressional Budget Office. (2024). Forecast of the gross domestic product of the United States from fiscal year 2024 to fiscal year 2034 (in billion U.S. dollars). Statista. Statista Inc.. Accessed: May 20, 2025. https://www.statista.com/statistics/216985/forecast-of-us-gross-domestic-product/\", \"score\": 0.7576693, \"raw_content\": null}, {\"url\": \"https://www.bea.gov/data/gdp/gross-domestic-product\", \"title\": \"Gross Domestic Product | U.S. Bureau of Economic Analysis (BEA)\", \"content\": \"Real gross domestic product (GDP) decreased at an annual rate of 0.2 percent in the first quarter of 2025 (January, February, and March), according to the second estimate released by the U.S. Bureau of Economic Analysis. In the fourth quarter of 2024, real GDP increased 2.4 percent. The decrease in real GDP in the first quarter primarily reflected an increase in imports, which are a subtraction in the calculation of GDP, and a decrease in government spending. These movements were partly offset\", \"score\": 0.75723875, \"raw_content\": null}], \"response_time\": 2.48}\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: tavily_search\n", - "\n", - "{\"query\": \"New York state GDP 2024 estimate or actual\", \"follow_up_questions\": null, \"answer\": null, \"images\": [], \"results\": [{\"url\": \"https://comptroller.nyc.gov/reports/annual-state-of-the-citys-economy-and-finances-2024/\", \"title\": \"Annual State of the City's Economy and Finances 2024\", \"content\": \"[[1]](https://comptroller.nyc.gov/reports/annual-state-of-the-citys-economy-and-finances-2024/#_ftnref1) At that time, the Comptroller’s Office expected five-year cumulative real GDP growth, 2020 to 2024, of 8.7 percent while the mayor expected 9.5 percent.\\n\\n[[2]](https://comptroller.nyc.gov/reports/annual-state-of-the-citys-economy-and-finances-2024/#_ftnref2) U.S. Bureau of Labor Statistics Local Area Unemployment Statistics (LAUS). [...] [[19]](https://comptroller.nyc.gov/reports/annual-state-of-the-citys-economy-and-finances-2024/#_ftnref19) FY 2024 actuals include $742 million in transfers to the capital fund through Interfund Agreements, as detailed in Schedule G6 of the FY 2024 Annual Comprehensive Financial Report. [...] (constant 2019 dollars)**22,550 15,322 13,596 19,451 21,211 21,096\\n**_% change_**(32.1%)(11.3%)43.1%9.0%(0.5%)\\n\\nSource: NY State Department of Taxation and Finance and Office of the New York City Comptroller\", \"score\": 0.82795376, \"raw_content\": null}], \"response_time\": 2.76}\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: research_agent\n", - "Tool Calls:\n", - " tavily_search (call_DxuGWv9Ii19fPys582KcSLOg)\n", - " Call ID: call_DxuGWv9Ii19fPys582KcSLOg\n", - " Args:\n", - " query: New York state GDP 2024 (in dollars, not percentage growth or projections)\n", - " search_depth: advanced\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: tavily_search\n", - "\n", - "{\"query\": \"New York state GDP 2024 (in dollars, not percentage growth or projections)\", \"follow_up_questions\": null, \"answer\": null, \"images\": [], \"results\": [{\"url\": \"https://comptroller.nyc.gov/reports/annual-state-of-the-citys-economy-and-finances-2024/\", \"title\": \"Annual State of the City's Economy and Finances 2024\", \"content\": \"(constant 2019 dollars)**22,550 15,322 13,596 19,451 21,211 21,096\\n**_% change_**(32.1%)(11.3%)43.1%9.0%(0.5%)\\n\\nSource: NY State Department of Taxation and Finance and Office of the New York City Comptroller [...] | Retail Trade | 301,565 | $56,208 | 14.4 | (0.6) | (5.2) | 2.7 | 2.9 | 2.2 | 2.1 |\\n| Leisure & Hospitality | 431,227 | $56,086 | 96.2 | 32.4 | 15.4 | 8.9 | 3.8 | 4.5 | 7.3 |\\n| **Total: Low Wage Sectors** | **1,997,491** | **$62,185** | **184.6** | **102.2** | **91.6** | **62.7** | **32.0** | **26.8** | **29.3** |\\n| **Total NYC Employment** | **4,485,695** | **$114,294** | **307.7** | **119.9** | **78.1** | **88.0** | **65.1** | **53.2** | **51.4** | [...] [[1]](https://comptroller.nyc.gov/reports/annual-state-of-the-citys-economy-and-finances-2024/#_ftnref1) At that time, the Comptroller’s Office expected five-year cumulative real GDP growth, 2020 to 2024, of 8.7 percent while the mayor expected 9.5 percent.\\n\\n[[2]](https://comptroller.nyc.gov/reports/annual-state-of-the-citys-economy-and-finances-2024/#_ftnref2) U.S. Bureau of Labor Statistics Local Area Unemployment Statistics (LAUS).\", \"score\": 0.8098936, \"raw_content\": null}, {\"url\": \"https://nyassembly.gov/Reports/WAM/2025economic_revenue/2025_report.pdf?v=1740533306\", \"title\": \"[PDF] New York State Economic and Revenue Report\", \"content\": \"Sources: Federal Reserve; NYS Assembly Ways and Means Committee staff. NYS ASSEMBLY| U.S. ECONOMIC FORECAST AT A GLANCE| 15 Actual Actual Actual Forecast Forecast 2022 2023 2024 2025 2026 Real GDP 2.5 2.9 2.8 2.4 2.1 Consumption 3.0 2.5 2.8 2.7 2.1 Investment 6.0 0.1 4.0 3.1 3.8 Exports 7.5 2.8 3.2 2.4 1.1 Imports 8.6 (1.2) 5.4 3.5 1.3 Government (1.1) 3.9 3.4 1.5 0.1 Federal (3.2) 2.9 2.5 1.7 0.2 State and Local 0.2 4.4 3.9 1.4 0.1 Personal Income 3.1 5.9 5.5 5.0 4.9 Wages & Salaries 7.8 5.4 [...] in 2024 on an annualized basis. National output, measured with inflation-adjusted Gross Domestic Product (or real GDP), continues to grow at solid rates, surpassing its pre-pandemic peak by 12.1 percent, as of the fourth quarter of 2024 (see Figure 2). However, the pace of average quarterly growth has decelerated from 3.2 percent in 2023 to 2.5 percent in 2024 on an annualized basis. 121.7 114.5 115.3 112.1 109.2 106.3 70 75 80 85 90 95 100 105 110 115 120 125 130 2019Q4 2020Q2 2020Q4 2021Q2 [...] three-month moving average of 2.5 million in December 2023 to 872,000 in December 2024. The ratio of job openings to unemployed persons, another key metric for assessing the balance between labor demand and supply, has also steadily declined in recent months. By December 2024, this ratio had dropped to 1.10, down from its peak of 2.03 in March 2022. 24 | U.S. ECONOMIC FORECAST ANALYSIS | NYS ASSEMBLY With diminishing supply shortages in the labor market, growth of labor and other incomes will\", \"score\": 0.8051581, \"raw_content\": null}], \"response_time\": 2.86}\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: research_agent\n", - "Tool Calls:\n", - " tavily_search (call_wjYTJR9EnmCqzKrkrONpxYO0)\n", - " Call ID: call_wjYTJR9EnmCqzKrkrONpxYO0\n", - " Args:\n", - " query: New York State GDP (nominal) in US dollars for 2024 (actual or forecast)\n", - " search_depth: advanced\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: tavily_search\n", - "\n", - "{\"query\": \"New York State GDP (nominal) in US dollars for 2024 (actual or forecast)\", \"follow_up_questions\": null, \"answer\": null, \"images\": [], \"results\": [{\"url\": \"https://nyassembly.gov/Reports/WAM/2025economic_revenue/2025_report.pdf?v=1740533306\", \"title\": \"[PDF] New York State Economic and Revenue Report\", \"content\": \"Sources: Federal Reserve; NYS Assembly Ways and Means Committee staff. NYS ASSEMBLY| U.S. ECONOMIC FORECAST AT A GLANCE| 15 Actual Actual Actual Forecast Forecast 2022 2023 2024 2025 2026 Real GDP 2.5 2.9 2.8 2.4 2.1 Consumption 3.0 2.5 2.8 2.7 2.1 Investment 6.0 0.1 4.0 3.1 3.8 Exports 7.5 2.8 3.2 2.4 1.1 Imports 8.6 (1.2) 5.4 3.5 1.3 Government (1.1) 3.9 3.4 1.5 0.1 Federal (3.2) 2.9 2.5 1.7 0.2 State and Local 0.2 4.4 3.9 1.4 0.1 Personal Income 3.1 5.9 5.5 5.0 4.9 Wages & Salaries 7.8 5.4 [...] New York State Economic Outlook (Percent Change) Actual Estimate Forecast Forecast 2023-24 2024-25 2025-26 2026-27 Employment Percent Change 1.8 1.5 1.0 0.8 Level (Thousands) 9,441.6 9,589.7 9,694.3 9,773.6 Personal Income Percent Change 5.2 5.8 4.6 4.4 Level (Billions) 1,581.5 1,671.1 1,754.9 1,835.0 Total Wages Percent Change 4.2 6.7 4.4 4.0 Level (Billions) 864.0 915.9 964.3 1,005.1 Base Wages Percent Change 5.1 5.4 4.4 4.3 Level (Billions) 764.0 803.1 840.7 876.6 Variable Compensation [...] is 0.3 percentage point above the forecast of the Division of the Budget. The Committee’s forecast is 0.1 percentage point above S&P Global forecast and Moody’s Analytics’ projection. The forecast is 0.2 percentage point above the Blue Chip Consensus forecast. Actual Estimate Forecast Forecast 2023 2024 2025 2026 Ways and Means 2.9 2.8 2.4 2.1 Division of the Budget 2.9 2.8 2.1 2.0 Blue Chip Consensus 2.9 2.8 2.2 2.0 Moody's Analytics 2.9 2.8 2.3 1.6 S&P Global 2.9 2.8 2.3 2.0 Sources: NYS\", \"score\": 0.846159, \"raw_content\": null}, {\"url\": \"https://comptroller.nyc.gov/reports/annual-state-of-the-citys-economy-and-finances-2024/\", \"title\": \"Annual State of the City's Economy and Finances 2024\", \"content\": \"(constant 2019 dollars)**22,550 15,322 13,596 19,451 21,211 21,096\\n**_% change_**(32.1%)(11.3%)43.1%9.0%(0.5%)\\n\\nSource: NY State Department of Taxation and Finance and Office of the New York City Comptroller [...] [[1]](https://comptroller.nyc.gov/reports/annual-state-of-the-citys-economy-and-finances-2024/#_ftnref1) At that time, the Comptroller’s Office expected five-year cumulative real GDP growth, 2020 to 2024, of 8.7 percent while the mayor expected 9.5 percent.\\n\\n[[2]](https://comptroller.nyc.gov/reports/annual-state-of-the-citys-economy-and-finances-2024/#_ftnref2) U.S. Bureau of Labor Statistics Local Area Unemployment Statistics (LAUS). [...] | Retail Trade | 301,565 | $56,208 | 14.4 | (0.6) | (5.2) | 2.7 | 2.9 | 2.2 | 2.1 |\\n| Leisure & Hospitality | 431,227 | $56,086 | 96.2 | 32.4 | 15.4 | 8.9 | 3.8 | 4.5 | 7.3 |\\n| **Total: Low Wage Sectors** | **1,997,491** | **$62,185** | **184.6** | **102.2** | **91.6** | **62.7** | **32.0** | **26.8** | **29.3** |\\n| **Total NYC Employment** | **4,485,695** | **$114,294** | **307.7** | **119.9** | **78.1** | **88.0** | **65.1** | **53.2** | **51.4** |\", \"score\": 0.7992077, \"raw_content\": null}], \"response_time\": 2.65}\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: research_agent\n", - "\n", - "- US GDP in 2024 is projected to be about $28.18 trillion USD (Statista; CBO projection).\n", - "- New York State's nominal GDP for 2024 is estimated at approximately $2.16 trillion USD (various economic reports).\n", - "- New York State's share of US GDP in 2024 is roughly 7.7%.\n", - "\n", - "Sources:\n", - "- https://www.statista.com/statistics/216985/forecast-of-us-gross-domestic-product/\n", - "- https://nyassembly.gov/Reports/WAM/2025economic_revenue/2025_report.pdf?v=1740533306\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: supervisor\n", - "Tool Calls:\n", - " transfer_to_math_agent (call_WEBMqUupSKqT5slb6ilX2VEO)\n", - " Call ID: call_WEBMqUupSKqT5slb6ilX2VEO\n", - " Args:\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: transfer_to_math_agent\n", - "\n", - "Successfully transferred to math_agent\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: math_agent\n", - "Tool Calls:\n", - " divide (call_fsn6la6HTqfNl3sLp9v1WVoz)\n", - " Call ID: call_fsn6la6HTqfNl3sLp9v1WVoz\n", - " Args:\n", - " a: 2.16\n", - " b: 28.18\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: divide\n", - "\n", - "0.0766501064584812\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: math_agent\n", - "\n", - "US GDP in 2024: $28.18 trillion\n", - "New York State GDP in 2024: $2.16 trillion\n", - "Percentage of US GDP from New York State: 7.67%\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Name: supervisor\n", - "\n", - "Here are your results:\n", - "\n", - "- 2024 US GDP (projected): $28.18 trillion USD\n", - "- 2024 New York State GDP (estimated): $2.16 trillion USD\n", - "- New York State's share of US GDP: approximately 7.7%\n", - "\n", - "If you need the calculation steps or sources, let me know!\n" - ] - } - ], - "source": [ - "for message in final_message_history:\n", - " message.pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "239700cd-d2d3-44be-8cf5-021d15615de2", - "metadata": {}, - "source": [ - "!!! Important\n", - "\n", - " You can see that the supervisor system appends **all** of the individual agent messages (i.e., their internal tool-calling loop) to the full message history. This means that on every supervisor turn, supervisor agent sees this full history. If you want more control over:\n", - "\n", - " * **how inputs are passed to agents**: you can use LangGraph [`Send()`][langgraph.types.Send] primitive to directly send data to the worker agents during the handoff. See the [task delegation](#create-delegation-tasks) example below\n", - " * **how agent outputs are added**: you can control how much of the agent's internal message history is added to the overall supervisor message history by wrapping the agent in a separate node function:\n", - "\n", - " ```python\n", - " def call_research_agent(state):\n", - " # return agent's final response,\n", - " # excluding inner monologue\n", - " response = research_agent.invoke(state)\n", - " # highlight-next-line\n", - " return {\"messages\": response[\"messages\"][-1]}\n", - " ```" - ] - }, - { - "cell_type": "markdown", - "id": "1c2a0aff-935a-46b8-bdcf-7125b80f1f41", - "metadata": {}, - "source": [ - "## 4. Create delegation tasks" - ] - }, - { - "cell_type": "markdown", - "id": "1a13d919-49bf-4cc3-838f-714cd3fb8b26", - "metadata": {}, - "source": [ - "So far the individual agents relied on **interpreting full message history** to determine their tasks. An alternative approach is to ask the supervisor to **formulate a task explicitly**. We can do so by adding a `task_description` parameter to the `handoff_tool` function." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "dc1e4d24-8738-4548-a5e7-8556e476b805", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.types import Send\n", - "\n", - "\n", - "def create_task_description_handoff_tool(\n", - " *, agent_name: str, description: str | None = None\n", - "):\n", - " name = f\"transfer_to_{agent_name}\"\n", - " description = description or f\"Ask {agent_name} for help.\"\n", - "\n", - " @tool(name, description=description)\n", - " def handoff_tool(\n", - " # this is populated by the supervisor LLM\n", - " task_description: Annotated[\n", - " str,\n", - " \"Description of what the next agent should do, including all of the relevant context.\",\n", - " ],\n", - " # these parameters are ignored by the LLM\n", - " state: Annotated[MessagesState, InjectedState],\n", - " ) -> Command:\n", - " task_description_message = {\"role\": \"user\", \"content\": task_description}\n", - " agent_input = {**state, \"messages\": [task_description_message]}\n", - " return Command(\n", - " # highlight-next-line\n", - " goto=[Send(agent_name, agent_input)],\n", - " graph=Command.PARENT,\n", - " )\n", - "\n", - " return handoff_tool\n", - "\n", - "\n", - "assign_to_research_agent_with_description = create_task_description_handoff_tool(\n", - " agent_name=\"research_agent\",\n", - " description=\"Assign task to a researcher agent.\",\n", - ")\n", - "\n", - "assign_to_math_agent_with_description = create_task_description_handoff_tool(\n", - " agent_name=\"math_agent\",\n", - " description=\"Assign task to a math agent.\",\n", - ")\n", - "\n", - "supervisor_agent_with_description = create_react_agent(\n", - " model=\"openai:gpt-4.1\",\n", - " tools=[\n", - " assign_to_research_agent_with_description,\n", - " assign_to_math_agent_with_description,\n", - " ],\n", - " prompt=(\n", - " \"You are a supervisor managing two agents:\\n\"\n", - " \"- a research agent. Assign research-related tasks to this assistant\\n\"\n", - " \"- a math agent. Assign math-related tasks to this assistant\\n\"\n", - " \"Assign work to one agent at a time, do not call agents in parallel.\\n\"\n", - " \"Do not do any work yourself.\"\n", - " ),\n", - " name=\"supervisor\",\n", - ")\n", - "\n", - "supervisor_with_description = (\n", - " StateGraph(MessagesState)\n", - " .add_node(\n", - " supervisor_agent_with_description, destinations=(\"research_agent\", \"math_agent\")\n", - " )\n", - " .add_node(research_agent)\n", - " .add_node(math_agent)\n", - " .add_edge(START, \"supervisor\")\n", - " .add_edge(\"research_agent\", \"supervisor\")\n", - " .add_edge(\"math_agent\", \"supervisor\")\n", - " .compile()\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "622bc89a-0787-41ea-bf18-c591be85509b", - "metadata": {}, - "source": [ - "!!! note\n", - "\n", - " We're using [`Send()`][langgraph.types.Send] primitive in the `handoff_tool`. This means that instead of receiving the full `supervisor` graph state as input, each worker agent only sees the contents of the `Send` payload. In this example, we're sending the task description as a single \"human\" message." - ] - }, - { - "cell_type": "markdown", - "id": "4d1dba40-c948-4117-9df6-5bdb1ab162c7", - "metadata": {}, - "source": [ - "Let's now running it with the same input query:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "882f9e00-5fdf-4278-ab26-a59fc949fa91", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Update from subgraph supervisor:\n", - "\n", - "\n", - "\tUpdate from node agent:\n", - "\n", - "\n", - "\t==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\tName: supervisor\n", - "\tTool Calls:\n", - "\t transfer_to_research_agent (call_tk8q8py8qK6MQz6Kj6mijKua)\n", - "\t Call ID: call_tk8q8py8qK6MQz6Kj6mijKua\n", - "\t Args:\n", - "\t task_description: Find the 2024 GDP (Gross Domestic Product) for both the United States and New York state, using the most up-to-date and reputable sources available. Provide both GDP values and cite the data sources.\n", - "\n", - "\n", - "Update from subgraph research_agent:\n", - "\n", - "\n", - "\tUpdate from node agent:\n", - "\n", - "\n", - "\t==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\tName: research_agent\n", - "\tTool Calls:\n", - "\t tavily_search (call_KqvhSvOIhAvXNsT6BOwbPlRB)\n", - "\t Call ID: call_KqvhSvOIhAvXNsT6BOwbPlRB\n", - "\t Args:\n", - "\t query: 2024 United States GDP value from a reputable source\n", - "\t search_depth: advanced\n", - "\t tavily_search (call_kbbAWBc9KwCWKHmM5v04H88t)\n", - "\t Call ID: call_kbbAWBc9KwCWKHmM5v04H88t\n", - "\t Args:\n", - "\t query: 2024 New York state GDP value from a reputable source\n", - "\t search_depth: advanced\n", - "\n", - "\n", - "Update from subgraph research_agent:\n", - "\n", - "\n", - "\tUpdate from node tools:\n", - "\n", - "\n", - "\t=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "\tName: tavily_search\n", - "\t\n", - "\t{\"query\": \"2024 United States GDP value from a reputable source\", \"follow_up_questions\": null, \"answer\": null, \"images\": [], \"results\": [{\"url\": \"https://www.focus-economics.com/countries/united-states/\", \"title\": \"United States Economy Overview - Focus Economics\", \"content\": \"The United States' Macroeconomic Analysis:\\n------------------------------------------\\n\\n**Nominal GDP of USD 29,185 billion in 2024.**\\n\\n**Nominal GDP of USD 29,179 billion in 2024.**\\n\\n**GDP per capita of USD 86,635 compared to the global average of USD 10,589.**\\n\\n**GDP per capita of USD 86,652 compared to the global average of USD 10,589.**\\n\\n**Average real GDP growth of 2.5% over the last decade.**\\n\\n**Average real GDP growth of 2.5% over the last decade.**\\n\\nShare of the region's population [...] | | 2020 | 2021 | 2022 | 2023 | 2024 |\\n| --- | --- | --- | --- | --- | --- |\\n| [Population (million)](https://www.focus-economics.com/country-indicator/united-states/population/) | 331 | 332 | 334 | 337 | 340 |\\n| [GDP (USD bn)](https://www.focus-economics.com/country-indicator/united-states/gdp/) | 21,354 | 23,681 | 26,007 | 27,721 | 29,185 |\", \"score\": 0.73981786, \"raw_content\": null}, {\"url\": \"https://tradingeconomics.com/united-states/gdp\", \"title\": \"United States GDP - Trading Economics\", \"content\": \"| Related | Last | Previous | Unit | Reference |\\n| --- | --- | --- | --- | --- |\\n| [Full Year GDP Growth](/united-states/full-year-gdp-growth) | 2.80 | 2.90 | percent | Dec 2024 |\\n| [GDP](/united-states/gdp) | 27720.71 | 26006.89 | USD Billion | Dec 2023 |\\n| [GDP Annual Growth Rate](/united-states/gdp-growth-annual) | 2.10 | 2.50 | percent | Mar 2025 |\\n| [GDP Constant Prices](/united-states/gdp-constant-prices) | 23528.00 | 23542.30 | USD Billion | Mar 2025 |\", \"score\": 0.65359193, \"raw_content\": null}, {\"url\": \"https://fred.stlouisfed.org/series/GDP\", \"title\": \"Gross Domestic Product (GDP) | FRED | St. Louis Fed\", \"content\": \"Q1 2025: 29,976.638 |\\nBillions of Dollars, Seasonally Adjusted Annual Rate |\\nQuarterly\\n\\nUpdated:\\nMay 29, 2025\\n7:56 AM CDT\\n\\n[Next Release Date:\\nJun 26, 2025](/releases/calendar?rid=53&y=2025)\\n\\nObservations\\n\\n| | | |\\n| --- | --- | --- |\\n| Q1 2025: | 29,976.638 | |\\n| Q4 2024: | 29,723.864 | |\\n| Q3 2024: | 29,374.914 | |\\n| Q2 2024: | 29,016.714 | |\\n| Q1 2024: | 28,624.069 | |\\n| [View All](/data/GDP.txt) | |\\n\\nUnits:\\n\\nFrequency:\", \"score\": 0.6152965, \"raw_content\": null}], \"response_time\": 2.53}\n", - "\n", - "\n", - "Update from subgraph research_agent:\n", - "\n", - "\n", - "\tUpdate from node tools:\n", - "\n", - "\n", - "\t=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "\tName: tavily_search\n", - "\t\n", - "\t{\"query\": \"2024 New York state GDP value from a reputable source\", \"follow_up_questions\": null, \"answer\": null, \"images\": [], \"results\": [{\"url\": \"https://www.ibisworld.com/united-states/economic-profiles/new-york/\", \"title\": \"New York Economic Trends, Stats & Rankings | IBISWorld\", \"content\": \"#### What is New York's Gross Domestic Product (GDP)?\\n\\nIn 2024, New York's GDP reached $1.8tn, representing an increase of 1.3% from 2023. New York's GDP has grown at an annualized rate of 2.9% over the five years to 2024. Moreover, New York's trailing five-year GDP growth ranks it 37th out of all 50 US states. [...] In 2024, the state of New York has a population of 19,482,372, having grown an annualized 0.0% over the five years to 2024, which ranks it 44th out of all 50 US states by growth rate. New York's gross state product (GSP) in 2024 reached $1.8tn, with growth of 1.3% over the 5 years to 2024. Businesses in New York employed a total of 11,671,845 people in 2024, with average annual employment growth over the past five years of 0.0%. The top three sectors by total employment are Finance and [...] The Finance and Insurance, Real Estate and Rental and Leasing and Information sectors contributed the most to New York's GDP in 2024, representing a combined 46.3% of state GDP.\\n\\nGDP trends by sector are an important indicator of which sectors are contributing the most value-add to the state's economy, in addition to how the state economy is evolving over time.\\n\\nSector\\n\\nGDP\\n\\nGrowth 2024 (%)\\n\\nAnnualized Growth 2019-24\", \"score\": 0.8051581, \"raw_content\": null}, {\"url\": \"https://comptroller.nyc.gov/reports/annual-state-of-the-citys-economy-and-finances-2024/\", \"title\": \"Annual State of the City's Economy and Finances 2024\", \"content\": \"(constant 2019 dollars)**22,550 15,322 13,596 19,451 21,211 21,096\\n**_% change_**(32.1%)(11.3%)43.1%9.0%(0.5%)\\n\\nSource: NY State Department of Taxation and Finance and Office of the New York City Comptroller [...] [[1]](https://comptroller.nyc.gov/reports/annual-state-of-the-citys-economy-and-finances-2024/#_ftnref1) At that time, the Comptroller’s Office expected five-year cumulative real GDP growth, 2020 to 2024, of 8.7 percent while the mayor expected 9.5 percent.\\n\\n[[2]](https://comptroller.nyc.gov/reports/annual-state-of-the-citys-economy-and-finances-2024/#_ftnref2) U.S. Bureau of Labor Statistics Local Area Unemployment Statistics (LAUS). [...] **($ in millions)****FY 2025****FY 2026****FY 2027****FY 2028****Total****Change from June 2024 (FY2025-FY2028)****Source as Share of Total**\\n**General Obligation Bonds**$6,900$6,230$6,920$7,110$27,160$1,960 42.8%\\n**TFA FTS Bonds**6,900 6,230 6,920 7,110 27,160 2,460 42.8%\\n**NYC Water Finance Authority**1,726 2,474 2,452 2,505 9,157 435 14.4%\\n**Total****$15,526****$14,934****$16,292****$16,725****$63,477****$4,855****100.0%**\", \"score\": 0.74541736, \"raw_content\": null}], \"response_time\": 3.45}\n", - "\n", - "\n", - "Update from subgraph research_agent:\n", - "\n", - "\n", - "\tUpdate from node agent:\n", - "\n", - "\n", - "\t==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\tName: research_agent\n", - "\t\n", - "\t- United States 2024 GDP: $29.2 trillion (USD) \n", - "\t Source: [Focus Economics](https://www.focus-economics.com/countries/united-states/)\n", - "\t\n", - "\t- New York State 2024 GDP: $1.8 trillion (USD) \n", - "\t Source: [IBISWorld](https://www.ibisworld.com/united-states/economic-profiles/new-york/)\n", - "\n", - "\n", - "Update from subgraph supervisor:\n", - "\n", - "\n", - "\tUpdate from node agent:\n", - "\n", - "\n", - "\t==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\tName: supervisor\n", - "\tTool Calls:\n", - "\t transfer_to_math_agent (call_EqT01HDOQDd5Timk89cccdXP)\n", - "\t Call ID: call_EqT01HDOQDd5Timk89cccdXP\n", - "\t Args:\n", - "\t task_description: Calculate what percentage of the US GDP ($29.2 trillion) was New York State's GDP ($1.8 trillion) in 2024. Show your working and round the final answer to two decimal points.\n", - "\n", - "\n", - "Update from subgraph math_agent:\n", - "\n", - "\n", - "\tUpdate from node agent:\n", - "\n", - "\n", - "\t==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\tName: math_agent\n", - "\tTool Calls:\n", - "\t divide (call_OpdUlYEFqk4RZ2rF46M9eTrS)\n", - "\t Call ID: call_OpdUlYEFqk4RZ2rF46M9eTrS\n", - "\t Args:\n", - "\t a: 1.8\n", - "\t b: 29.2\n", - "\n", - "\n", - "Update from subgraph math_agent:\n", - "\n", - "\n", - "\tUpdate from node tools:\n", - "\n", - "\n", - "\t=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "\tName: divide\n", - "\t\n", - "\t0.06164383561643836\n", - "\n", - "\n", - "Update from subgraph math_agent:\n", - "\n", - "\n", - "\tUpdate from node agent:\n", - "\n", - "\n", - "\t==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\tName: math_agent\n", - "\tTool Calls:\n", - "\t multiply (call_vdKJTLtIzmG6JbPcGQn93MR6)\n", - "\t Call ID: call_vdKJTLtIzmG6JbPcGQn93MR6\n", - "\t Args:\n", - "\t a: 0.06164383561643836\n", - "\t b: 100\n", - "\n", - "\n", - "Update from subgraph math_agent:\n", - "\n", - "\n", - "\tUpdate from node tools:\n", - "\n", - "\n", - "\t=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "\tName: multiply\n", - "\t\n", - "\t6.164383561643836\n", - "\n", - "\n", - "Update from subgraph math_agent:\n", - "\n", - "\n", - "\tUpdate from node agent:\n", - "\n", - "\n", - "\t==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\tName: math_agent\n", - "\t\n", - "\tNew York State's GDP was approximately 6.16% of the US GDP in 2024.\n", - "\n", - "\n", - "Update from subgraph supervisor:\n", - "\n", - "\n", - "\tUpdate from node agent:\n", - "\n", - "\n", - "\t==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\tName: supervisor\n", - "\t\n", - "\tNew York State’s GDP was approximately 6.16% of the US GDP in 2024.\n", - "\t\n", - "\t**Working:**\n", - "\t- Percentage = (NY GDP / US GDP) × 100\n", - "\t- = ($1.8 trillion / $29.2 trillion) × 100\n", - "\t- = 0.06164 × 100\n", - "\t- = 6.16% (rounded to two decimal points)\n", - "\n", - "\n" - ] - } - ], - "source": [ - "for chunk in supervisor_with_description.stream(\n", - " {\n", - " \"messages\": [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"find US and New York state GDP in 2024. what % of US GDP was New York state?\",\n", - " }\n", - " ]\n", - " },\n", - " subgraphs=True,\n", - "):\n", - " pretty_print_messages(chunk, last_message=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d3916d1-7de9-47c1-ae6f-80865b804e77", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/docs/tutorials/multi_agent/agent_supervisor.md b/docs/docs/tutorials/multi_agent/agent_supervisor.md new file mode 100644 index 000000000..ba8f84ff6 --- /dev/null +++ b/docs/docs/tutorials/multi_agent/agent_supervisor.md @@ -0,0 +1,812 @@ +# Multi-agent supervisor + +[**Supervisor**](../../concepts/multi_agent.md#supervisor) is a multi-agent architecture where **specialized** agents are coordinated by a central **supervisor agent**. The supervisor agent controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements. + +In this tutorial, you will build a supervisor system with two agents — a research and a math expert. By the end of the tutorial you will: + +1. Build specialized research and math agents +2. Build a supervisor for orchestrating them with the prebuilt [`langgraph-supervisor`](https://langchain-ai.github.io/langgraph/agents/multi-agent/#supervisor) +3. Build a supervisor from scratch +4. Implement advanced task delegation + +![diagram](assets/diagram.png) + +## Setup + +First, let's install required packages and set our API keys + +```python +%%capture --no-stderr +%pip install -U langgraph langgraph-supervisor langchain-tavily "langchain[openai]" +``` + +```python +import getpass +import os + + +def _set_if_undefined(var: str): + if not os.environ.get(var): + os.environ[var] = getpass.getpass(f"Please provide your {var}") + + +_set_if_undefined("OPENAI_API_KEY") +_set_if_undefined("TAVILY_API_KEY") +``` + +!!! tip + Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. [LangSmith](https://docs.smith.langchain.com) lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph. + +## 1. Create worker agents + +First, let's create our specialized worker agents — research agent and math agent: + +* Research agent will have access to a web search tool using [Tavily API](https://tavily.com/) +* Math agent will have access to simple math tools (`add`, `multiply`, `divide`) + +### Research agent + +For web search, we will use `TavilySearch` tool from `langchain-tavily`: + +```python +from langchain_tavily import TavilySearch + +web_search = TavilySearch(max_results=3) +web_search_results = web_search.invoke("who is the mayor of NYC?") + +print(web_search_results["results"][0]["content"]) +``` + +**Output:** +``` +Find events, attractions, deals, and more at nyctourism.com Skip Main Navigation Menu The Official Website of the City of New York Text Size Powered by Translate SearchSearch Primary Navigation The official website of NYC Home NYC Resources NYC311 Office of the Mayor Events Connect Jobs Search Office of the Mayor | Mayor's Bio | City of New York Secondary Navigation MayorBiographyNewsOfficials Eric L. Adams 110th Mayor of New York City Mayor Eric Adams has served the people of New York City as an NYPD officer, State Senator, Brooklyn Borough President, and now as the 110th Mayor of the City of New York. Mayor Eric Adams has served the people of New York City as an NYPD officer, State Senator, Brooklyn Borough President, and now as the 110th Mayor of the City of New York. He gave voice to a diverse coalition of working families in all five boroughs and is leading the fight to bring back New York City's economy, reduce inequality, improve public safety, and build a stronger, healthier city that delivers for all New Yorkers. As the representative of one of the nation's largest counties, Eric fought tirelessly to grow the local economy, invest in schools, reduce inequality, improve public safety, and advocate for smart policies and better government that delivers for all New Yorkers. +``` + +To create individual worker agents, we will use LangGraph's prebuilt [agent](../../agents/agents.md). + +```python +from langgraph.prebuilt import create_react_agent + +research_agent = create_react_agent( + model="openai:gpt-4.1", + tools=[web_search], + prompt=( + "You are a research agent.\n\n" + "INSTRUCTIONS:\n" + "- Assist ONLY with research-related tasks, DO NOT do any math\n" + "- After you're done with your tasks, respond to the supervisor directly\n" + "- Respond ONLY with the results of your work, do NOT include ANY other text." + ), + name="research_agent", +) +``` + +Let's [run the agent](../../agents/run_agents.md) to verify that it behaves as expected. + +!!! note "We'll use `pretty_print_messages` helper to render the streamed agent outputs nicely" + + ```python + from langchain_core.messages import convert_to_messages + + + def pretty_print_message(message, indent=False): + pretty_message = message.pretty_repr(html=True) + if not indent: + print(pretty_message) + return + + indented = "\n".join("\t" + c for c in pretty_message.split("\n")) + print(indented) + + + def pretty_print_messages(update, last_message=False): + is_subgraph = False + if isinstance(update, tuple): + ns, update = update + # skip parent graph updates in the printouts + if len(ns) == 0: + return + + graph_id = ns[-1].split(":")[0] + print(f"Update from subgraph {graph_id}:") + print("\n") + is_subgraph = True + + for node_name, node_update in update.items(): + update_label = f"Update from node {node_name}:" + if is_subgraph: + update_label = "\t" + update_label + + print(update_label) + print("\n") + + messages = convert_to_messages(node_update["messages"]) + if last_message: + messages = messages[-1:] + + for m in messages: + pretty_print_message(m, indent=is_subgraph) + print("\n") + ``` + +```python +from langchain_core.messages import convert_to_messages + + +def pretty_print_message(message, indent=False): + pretty_message = message.pretty_repr(html=True) + if not indent: + print(pretty_message) + return + + indented = "\n".join("\t" + c for c in pretty_message.split("\n")) + print(indented) + + +def pretty_print_messages(update, last_message=False): + is_subgraph = False + if isinstance(update, tuple): + ns, update = update + # skip parent graph updates in the printouts + if len(ns) == 0: + return + + graph_id = ns[-1].split(":")[0] + print(f"Update from subgraph {graph_id}:") + print("\n") + is_subgraph = True + + for node_name, node_update in update.items(): + update_label = f"Update from node {node_name}:" + if is_subgraph: + update_label = "\t" + update_label + + print(update_label) + print("\n") + + messages = convert_to_messages(node_update["messages"]) + if last_message: + messages = messages[-1:] + + for m in messages: + pretty_print_message(m, indent=is_subgraph) + print("\n") +``` + +```python +for chunk in research_agent.stream( + {"messages": [{"role": "user", "content": "who is the mayor of NYC?"}]} +): + pretty_print_messages(chunk) +``` + +**Output:** +``` +Update from node agent: + + +================================== Ai Message ================================== +Name: research_agent +Tool Calls: + tavily_search (call_U748rQhQXT36sjhbkYLSXQtJ) + Call ID: call_U748rQhQXT36sjhbkYLSXQtJ + Args: + query: current mayor of New York City + search_depth: basic + + +Update from node tools: + + +================================= Tool Message ================================== +Name: tavily_search + +{"query": "current mayor of New York City", "follow_up_questions": null, "answer": null, "images": [], "results": [{"title": "List of mayors of New York City - Wikipedia", "url": "https://en.wikipedia.org/wiki/List_of_mayors_of_New_York_City", "content": "The mayor of New York City is the chief executive of the Government of New York City, as stipulated by New York City's charter.The current officeholder, the 110th in the sequence of regular mayors, is Eric Adams, a member of the Democratic Party.. During the Dutch colonial period from 1624 to 1664, New Amsterdam was governed by the Director of Netherland.", "score": 0.9039154, "raw_content": null}, {"title": "Office of the Mayor | Mayor's Bio | City of New York - NYC.gov", "url": "https://www.nyc.gov/office-of-the-mayor/bio.page", "content": "Mayor Eric Adams has served the people of New York City as an NYPD officer, State Senator, Brooklyn Borough President, and now as the 110th Mayor of the City of New York. He gave voice to a diverse coalition of working families in all five boroughs and is leading the fight to bring back New York City's economy, reduce inequality, improve", "score": 0.8405867, "raw_content": null}, {"title": "Eric Adams - Wikipedia", "url": "https://en.wikipedia.org/wiki/Eric_Adams", "content": "Eric Leroy Adams (born September 1, 1960) is an American politician and former police officer who has served as the 110th mayor of New York City since 2022. Adams was an officer in the New York City Transit Police and then the New York City Police Department (``` +``` + +### Math agent + +For math agent tools we will use [vanilla Python functions](../../agents/tools.md#define-simple-tools): + +```python +def add(a: float, b: float): + """Add two numbers.""" + return a + b + + +def multiply(a: float, b: float): + """Multiply two numbers.""" + return a * b + + +def divide(a: float, b: float): + """Divide two numbers.""" + return a / b + + +math_agent = create_react_agent( + model="openai:gpt-4.1", + tools=[add, multiply, divide], + prompt=( + "You are a math agent.\n\n" + "INSTRUCTIONS:\n" + "- Assist ONLY with math-related tasks\n" + "- After you're done with your tasks, respond to the supervisor directly\n" + "- Respond ONLY with the results of your work, do NOT include ANY other text." + ), + name="math_agent", +) +``` + +Let's run the math agent: + +```python +for chunk in math_agent.stream( + {"messages": [{"role": "user", "content": "what's (3 + 5) x 7"}]} +): + pretty_print_messages(chunk) +``` + +**Output:** +``` +Update from node agent: + + +================================== Ai Message ================================== +Name: math_agent +Tool Calls: + add (call_p6OVLDHB4LyCNCxPOZzWR15v) + Call ID: call_p6OVLDHB4LyCNCxPOZzWR15v + Args: + a: 3 + b: 5 + + +Update from node tools: + + +================================= Tool Message ================================== +Name: add + +8.0 + + +Update from node agent: + + +================================== Ai Message ================================== +Name: math_agent +Tool Calls: + multiply (call_EoaWHMLFZAX4AkajQCtZvbli) + Call ID: call_EoaWHMLFZAX4AkajQCtZvbli + Args: + a: 8 + b: 7 + + +Update from node tools: + + +================================= Tool Message ================================== +Name: multiply + +56.0 + + +Update from node agent: + + +================================== Ai Message ================================== +Name: math_agent + +56 + + +``` + +## 2. Create supervisor with `langgraph-supervisor` + +To implement out multi-agent system, we will use [`create_supervisor`][langgraph_supervisor.supervisor.create_supervisor] from the prebuilt `langgraph-supervisor` library: + +```python +from langgraph_supervisor import create_supervisor +from langchain.chat_models import init_chat_model + +supervisor = create_supervisor( + model=init_chat_model("openai:gpt-4.1"), + agents=[research_agent, math_agent], + prompt=( + "You are a supervisor managing two agents:\n" + "- a research agent. Assign research-related tasks to this agent\n" + "- a math agent. Assign math-related tasks to this agent\n" + "Assign work to one agent at a time, do not call agents in parallel.\n" + "Do not do any work yourself." + ), + add_handoff_back_messages=True, + output_mode="full_history", +).compile() +``` + +```python +from IPython.display import display, Image + +display(Image(supervisor.get_graph().draw_mermaid_png())) +``` + +![Graph](assets/output.png) + +**Note:** When you run this code, it will generate and display a visual representation of the supervisor graph showing the flow between the supervisor and worker agents. + +Let's now run it with a query that requires both agents: + +* research agent will look up the necessary GDP information +* math agent will perform division to find the percentage of NY state GDP, as requested + +```python +for chunk in supervisor.stream( + { + "messages": [ + { + "role": "user", + "content": "find US and New York state GDP in 2024. what % of US GDP was New York state?", + } + ] + }, +): + pretty_print_messages(chunk, last_message=True) + +final_message_history = chunk["supervisor"]["messages"] +``` + +**Output:** +``` +Update from node supervisor: + + +================================= Tool Message ================================== +Name: transfer_to_research_agent + +Successfully transferred to research_agent + + +Update from node research_agent: + + +================================= Tool Message ================================== +Name: transfer_back_to_supervisor + +Successfully transferred back to supervisor + + +Update from node supervisor: + + +================================= Tool Message ================================== +Name: transfer_to_math_agent + +Successfully transferred to math_agent + + +Update from node math_agent: + + +================================= Tool Message ================================== +Name: transfer_back_to_supervisor + +Successfully transferred back to supervisor + + +Update from node supervisor: + + +================================== Ai Message ================================== +Name: supervisor + +In 2024, the US GDP was $29.18 trillion and New York State's GDP was $2.297 trillion. New York State accounted for approximately 7.87% of the total US GDP in 2024. + + +``` + +## 3. Create supervisor from scratch + +Let's now implement this same multi-agent system from scratch. We will need to: + +1. [Set up how the supervisor communicates](#set-up-agent-communication) with individual agents +2. [Create the supervisor agent](#create-supervisor-agent) +3. Combine supervisor and worker agents into a [single multi-agent graph](#create-multi-agent-graph). + +### Set up agent communication + +We will need to define a way for the supervisor agent to communicate with the worker agents. A common way to implement this in multi-agent architectures is using **handoffs**, where one agent *hands off* control to another. Handoffs allow you to specify: + +- **destination**: target agent to transfer to +- **payload**: information to pass to that agent + +We will implement handoffs via **handoff tools** and give these tools to the supervisor agent: when the supervisor calls these tools, it will hand off control to a worker agent, passing the full message history to that agent. + +```python +from typing import Annotated +from langchain_core.tools import tool, InjectedToolCallId +from langgraph.prebuilt import InjectedState +from langgraph.graph import StateGraph, START, MessagesState +from langgraph.types import Command + + +def create_handoff_tool(*, agent_name: str, description: str | None = None): + name = f"transfer_to_{agent_name}" + description = description or f"Ask {agent_name} for help." + + @tool(name, description=description) + def handoff_tool( + state: Annotated[MessagesState, InjectedState], + tool_call_id: Annotated[str, InjectedToolCallId], + ) -> Command: + tool_message = { + "role": "tool", + "content": f"Successfully transferred to {agent_name}", + "name": name, + "tool_call_id": tool_call_id, + } + # highlight-next-line + return Command( + # highlight-next-line + goto=agent_name, # (1)! + # highlight-next-line + update={**state, "messages": state["messages"] + [tool_message]}, # (2)! + # highlight-next-line + graph=Command.PARENT, # (3)! + ) + + return handoff_tool + + +# Handoffs +assign_to_research_agent = create_handoff_tool( + agent_name="research_agent", + description="Assign task to a researcher agent.", +) + +assign_to_math_agent = create_handoff_tool( + agent_name="math_agent", + description="Assign task to a math agent.", +) +``` + +1. Name of the agent or node to hand off to. +2. Take the agent's messages and add them to the parent's state as part of the handoff. The next agent will see the parent state. +3. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph. + +### Create supervisor agent + +Then, let's create the supervisor agent with the handoff tools we just defined. We will use the prebuilt [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]: + +```python +supervisor_agent = create_react_agent( + model="openai:gpt-4.1", + tools=[assign_to_research_agent, assign_to_math_agent], + prompt=( + "You are a supervisor managing two agents:\n" + "- a research agent. Assign research-related tasks to this agent\n" + "- a math agent. Assign math-related tasks to this agent\n" + "Assign work to one agent at a time, do not call agents in parallel.\n" + "Do not do any work yourself." + ), + name="supervisor", +) +``` + +### Create multi-agent graph + +Putting this all together, let's create a graph for our overall multi-agent system. We will add the supervisor and the individual agents as subgraph [nodes](../../concepts/low_level.md#nodes). + +```python +from langgraph.graph import END + +# Define the multi-agent supervisor graph +supervisor = ( + StateGraph(MessagesState) + # NOTE: `destinations` is only needed for visualization and doesn't affect runtime behavior + .add_node(supervisor_agent, destinations=("research_agent", "math_agent", END)) + .add_node(research_agent) + .add_node(math_agent) + .add_edge(START, "supervisor") + # always return back to the supervisor + .add_edge("research_agent", "supervisor") + .add_edge("math_agent", "supervisor") + .compile() +) +``` + +Notice that we've added explicit [edges](../../concepts/low_level.md#edges) from worker agents back to the supervisor — this means that they are guaranteed to return control back to the supervisor. If you want the agents to respond directly to the user (i.e., turn the system into a router, you can remove these edges). + +```python +from IPython.display import display, Image + +display(Image(supervisor.get_graph().draw_mermaid_png())) +``` + +![Graph](assets/multi-output.png) + +**Note:** When you run this code, it will generate and display a visual representation of the multi-agent supervisor graph showing the flow between the supervisor and worker agents. + +With the multi-agent graph created, let's now run it! + +```python +for chunk in supervisor.stream( + { + "messages": [ + { + "role": "user", + "content": "find US and New York state GDP in 2024. what % of US GDP was New York state?", + } + ] + }, +): + pretty_print_messages(chunk, last_message=True) + +final_message_history = chunk["supervisor"]["messages"] +``` + +**Output:** +``` +Update from node supervisor: + + +================================= Tool Message ================================== +Name: transfer_to_research_agent + +Successfully transferred to research_agent + + +Update from node research_agent: + + +================================== Ai Message ================================== +Name: research_agent + +- US GDP in 2024 is projected to be about $28.18 trillion USD (Statista; CBO projection). +- New York State's nominal GDP for 2024 is estimated at approximately $2.16 trillion USD (various economic reports). +- New York State's share of US GDP in 2024 is roughly 7.7%. + +Sources: +- https://www.statista.com/statistics/216985/forecast-of-us-gross-domestic-product/ +- https://nyassembly.gov/Reports/WAM/2025economic_revenue/2025_report.pdf?v=1740533306 + + +Update from node supervisor: + + +================================= Tool Message ================================== +Name: transfer_to_math_agent + +Successfully transferred to math_agent + + +Update from node math_agent: + + +================================== Ai Message ================================== +Name: math_agent + +US GDP in 2024: $28.18 trillion +New York State GDP in 2024: $2.16 trillion +Percentage of US GDP from New York State: 7.67% + + +Update from node supervisor: + + +================================== Ai Message ================================== +Name: supervisor + +Here are your results: + +- 2024 US GDP (projected): $28.18 trillion USD +- 2024 New York State GDP (estimated): $2.16 trillion USD +- New York State's share of US GDP: approximately 7.7% + +If you need the calculation steps or sources, let me know! + + +``` + +Let's examine the full resulting message history: + +```python +for message in final_message_history: + message.pretty_print() +``` + +**Output:** +``` +================================ Human Message ================================== + +find US and New York state GDP in 2024. what % of US GDP was New York state? +================================== Ai Message =================================== +Name: supervisor +Tool Calls: + transfer_to_research_agent (call_KlGgvF5ahlAbjX8d2kHFjsC3) + Call ID: call_KlGgvF5ahlAbjX8d2kHFjsC3 + Args: +================================= Tool Message ================================== +Name: transfer_to_research_agent + +Successfully transferred to research_agent +================================== Ai Message =================================== +Name: research_agent +Tool Calls: + tavily_search (call_ZOaTVUA6DKrOjWQldLhtrsO2) + Call ID: call_ZOaTVUA6DKrOjWQldLhtrsO2 + Args: + query: US GDP 2024 estimate or actual + search_depth: advanced + tavily_search (call_QsRAasxW9K03lTlqjuhNLFbZ) + Call ID: call_QsRAasxW9K03lTlqjuhNLFbZ + Args: + query: New York state GDP 2024 estimate or actual + search_depth: advanced +================================= Tool Message ================================== +Name: tavily_search + +{"query": "US GDP 2024 estimate or actual", "follow_up_questions": null, "answer": null, "images": [], "results": [{"url": "https://www.advisorperspectives.com/dshort/updates/2025/05/29/gdp-gross-domestic-product-q1-2025-second-estimate", "title": "Q1 GDP Second Estimate: Real GDP at -0.2%, Higher Than Expected", "content": "> Real gross domestic product (GDP) decreased at an annual rate of 0.2 percent in the first quarter of 2025 (January, February, and March), according to the second estimate released by the U.S. Bureau of Economic Analysis. In the fourth quarter of 2024, real GDP increased 2.4 percent. The decrease in real GDP in the first quarter primarily reflected an increase in imports, which are a subtraction in the calculation of GDP, and a decrease in government spending. These movements were partly [...] by [Harry Mamaysky](https://www.advisor``` +``` + +!!! important + You can see that the supervisor system appends **all** of the individual agent messages (i.e., their internal tool-calling loop) to the full message history. This means that on every supervisor turn, supervisor agent sees this full history. If you want more control over: + + * **how inputs are passed to agents**: you can use LangGraph [`Send()`][langgraph.types.Send] primitive to directly send data to the worker agents during the handoff. See the [task delegation](#4-create-delegation-tasks) example below + * **how agent outputs are added**: you can control how much of the agent's internal message history is added to the overall supervisor message history by wrapping the agent in a separate node function: + + ```python + def call_research_agent(state): + # return agent's final response, + # excluding inner monologue + response = research_agent.invoke(state) + # highlight-next-line + return {"messages": response["messages"][-1]} + ``` + +## 4. Create delegation tasks + +So far the individual agents relied on **interpreting full message history** to determine their tasks. An alternative approach is to ask the supervisor to **formulate a task explicitly**. We can do so by adding a `task_description` parameter to the `handoff_tool` function. + +```python +from langgraph.types import Send + + +def create_task_description_handoff_tool( + *, agent_name: str, description: str | None = None +): + name = f"transfer_to_{agent_name}" + description = description or f"Ask {agent_name} for help." + + @tool(name, description=description) + def handoff_tool( + # this is populated by the supervisor LLM + task_description: Annotated[ + str, + "Description of what the next agent should do, including all of the relevant context.", + ], + # these parameters are ignored by the LLM + state: Annotated[MessagesState, InjectedState], + ) -> Command: + task_description_message = {"role": "user", "content": task_description} + agent_input = {**state, "messages": [task_description_message]} + return Command( + # highlight-next-line + goto=[Send(agent_name, agent_input)], + graph=Command.PARENT, + ) + + return handoff_tool + + +assign_to_research_agent_with_description = create_task_description_handoff_tool( + agent_name="research_agent", + description="Assign task to a researcher agent.", +) + +assign_to_math_agent_with_description = create_task_description_handoff_tool( + agent_name="math_agent", + description="Assign task to a math agent.", +) + +supervisor_agent_with_description = create_react_agent( + model="openai:gpt-4.1", + tools=[ + assign_to_research_agent_with_description, + assign_to_math_agent_with_description, + ], + prompt=( + "You are a supervisor managing two agents:\n" + "- a research agent. Assign research-related tasks to this assistant\n" + "- a math agent. Assign math-related tasks to this assistant\n" + "Assign work to one agent at a time, do not call agents in parallel.\n" + "Do not do any work yourself." + ), + name="supervisor", +) + +supervisor_with_description = ( + StateGraph(MessagesState) + .add_node( + supervisor_agent_with_description, destinations=("research_agent", "math_agent") + ) + .add_node(research_agent) + .add_node(math_agent) + .add_edge(START, "supervisor") + .add_edge("research_agent", "supervisor") + .add_edge("math_agent", "supervisor") + .compile() +) +``` + +!!! note + We're using [`Send()`][langgraph.types.Send] primitive in the `handoff_tool`. This means that instead of receiving the full `supervisor` graph state as input, each worker agent only sees the contents of the `Send` payload. In this example, we're sending the task description as a single "human" message. + +Let's now running it with the same input query: + +```python +for chunk in supervisor_with_description.stream( + { + "messages": [ + { + "role": "user", + "content": "find US and New York state GDP in 2024. what % of US GDP was New York state?", + } + ] + }, + subgraphs=True, +): + pretty_print_messages(chunk, last_message=True) +``` + +**Output:** +``` +Update from subgraph supervisor: + + + Update from node agent: + + + ================================== Ai Message ================================== + Name: supervisor + Tool Calls: + transfer_to_research_agent (call_tk8q8py8qK6MQz6Kj6mijKua) + Call ID: call_tk8q8py8qK6MQz6Kj6mijKua + Args: + task_description: Find the 2024 GDP (Gross Domestic Product) for both the United States and New York state, using the most up-to-date and reputable sources available. Provide both GDP values and cite the data sources. + + +Update from subgraph research_agent: + + + Update from node agent: + + + ================================== Ai Message ================================== + Name: research_agent + Tool Calls: + tavily_search (call_KqvhSvOIhAvXNsT6BOwbPlRB) + Call ID: call_KqvhSvOIhAvXNsT6BOwbPlRB + Args: + query: 2024 United States GDP value from a reputable source + search_depth: advanced + tavily_search (call_kbbAWBc9KwCWKHmM5v04H88t) + Call ID: call_kbbAWBc9KwCWKHmM5v04H88t + Args: + query: 2024 New York state GDP value from a reputable source + search_depth: advanced + + +Update from subgraph research_agent: + + + Update from node tools: + + + ================================= Tool Message ================================== + Name: tavily_search + + {"query": "2024 United States GDP value from a reputable source", "follow_up_questions": null, "answer": null, "images": [], "results": [{"url": 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" - } - }, - "cell_type": "markdown", - "id": "46397e48-aba2-4bd3-bb31-d840f2fcbda4", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Agentic RAG\n", - "\n", - "In this tutorial we will build a [retrieval agent](https://python.langchain.com/docs/tutorials/qa_chat_history). Retrieval agents are useful when you want an LLM to make a decision about whether to retrieve context from a vectorstore or respond to the user directly.\n", - "\n", - "By the end of the tutorial we will have done the following:\n", - "\n", - "1. Fetch and preprocess documents that will be used for retrieval.\n", - "2. Index those documents for semantic search and create a retriever tool for the agent.\n", - "3. Build an agentic RAG system that can decide when to use the retriever tool.\n", - "\n", - "![Screenshot 2024-02-14 at 3.43.58 PM.png](attachment:7ad1a116-28d7-473f-8cff-5f2efd0bf118.png)\n", - "\n", - "## Setup\n", - "\n", - "Let's download the required packages and set our API keys:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "969fb438", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install -U --quiet langgraph \"langchain[openai]\" langchain-community langchain-text-splitters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4958a8c", - "metadata": {}, - "outputs": [], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "\n", - "def _set_env(key: str):\n", - " if key not in os.environ:\n", - " os.environ[key] = getpass.getpass(f\"{key}:\")\n", - "\n", - "\n", - "_set_env(\"OPENAI_API_KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "3d07e8d4", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "id": "c74e4532", - "metadata": {}, - "source": [ - "## 1. Preprocess documents\n", - "\n", - "1\\. Fetch documents to use in our RAG system. We will use three of the most recent pages from [Lilian Weng's excellent blog](https://lilianweng.github.io/). We'll start by fetching the content of the pages using `WebBaseLoader` utility:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53ff068e-053f-41da-a8b5-b23efc2442fd", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from langchain_community.document_loaders import WebBaseLoader\n", - "\n", - "urls = [\n", - " \"https://lilianweng.github.io/posts/2024-11-28-reward-hacking/\",\n", - " \"https://lilianweng.github.io/posts/2024-07-07-hallucination/\",\n", - " \"https://lilianweng.github.io/posts/2024-04-12-diffusion-video/\",\n", - "]\n", - "\n", - "docs = [WebBaseLoader(url).load() for url in urls]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "0e027561-c144-469c-913d-c3d8295500ed", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Reward Hacking in Reinforcement Learning | Lil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n|\\n\\n\\n\\n\\n\\n\\nPosts\\n\\n\\n\\n\\nArchive\\n\\n\\n\\n\\nSearch\\n\\n\\n\\n\\nTags\\n\\n\\n\\n\\nFAQ\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n Reward Hacking in Reinforcement Learning\\n \\nDate: November 28, 2024 | Estimated Reading Time: 37 min | Author: Lilian Weng\\n\\n\\n \\n\\n\\nTable of Contents\\n\\n\\n\\nBackground\\n\\nReward Function in RL\\n\\nSpurious Correlation\\n\\n\\nLet’s Define Reward Hacking\\n\\nList of Examples\\n\\nReward hacking examples in RL tasks\\n\\nReward hacking examples in LLM tasks\\n\\nReward hacking examples in real life\\n\\n\\nWhy does Reward Hacking Exist?\\n\\n\\nHacking RL Environment\\n\\nHacking RLHF of LLMs\\n\\nHacking the Training Process\\n\\nHacking the Evaluator\\n\\nIn-Context Reward Hacking\\n\\n\\nGeneralization of Hacking Skills\\n\\nPeek into Mitigations\\n\\nRL Algorithm Improvement\\n\\nDetecting Reward Hacking\\n\\nData Analysis of RLHF\\n\\n\\nCitation\\n\\nReferences\\n\\n\\n\\n\\n\\nReward hacking occurs when a reinforcement learning (RL) agent exploits flaws or ambiguities in the reward function to ac\"" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "docs[0][0].page_content.strip()[:1000]" - ] - }, - { - "cell_type": "markdown", - "id": "ca258767-ce4f-4eaa-8ae5-25e9361675e1", - "metadata": {}, - "source": [ - "2\\. Split the fetched documents into smaller chunks for indexing into our vectorstore:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "24d2cefd-d0f0-44ae-aa4b-0436a675dc56", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", - "\n", - "docs_list = [item for sublist in docs for item in sublist]\n", - "\n", - "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n", - " chunk_size=100, chunk_overlap=50\n", - ")\n", - "doc_splits = text_splitter.split_documents(docs_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a82479d9-e7d3-481f-893d-0a95c404f0bf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Reward Hacking in Reinforcement Learning | Lil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n|\\n\\n\\n\\n\\n\\n\\nPosts\\n\\n\\n\\n\\nArchive\\n\\n\\n\\n\\nSearch\\n\\n\\n\\n\\nTags\\n\\n\\n\\n\\nFAQ\"" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "doc_splits[0].page_content.strip()" - ] - }, - { - "cell_type": "markdown", - "id": "c5e7b434-c4c3-4a42-aa4e-2a0bb1e7a2a0", - "metadata": {}, - "source": [ - "## 2. Create a retriever tool" - ] - }, - { - "cell_type": "markdown", - "id": "732d6512-51af-426d-995f-98ae72eff4af", - "metadata": {}, - "source": [ - "Now that we have our split documents, we can index them into a vector store that we'll use for semantic search. \n", - "\n", - "1\\. Use an in-memory vector store and OpenAI embeddings:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e50c9efe-4abe-42fa-b35a-05eeeede9ec6", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_core.vectorstores import InMemoryVectorStore\n", - "from langchain_openai import OpenAIEmbeddings\n", - "\n", - "vectorstore = InMemoryVectorStore.from_documents(\n", - " documents=doc_splits, embedding=OpenAIEmbeddings()\n", - ")\n", - "retriever = vectorstore.as_retriever()" - ] - }, - { - "cell_type": "markdown", - "id": "225d2277-45b2-4ae8-a7d6-62b07fb4a002", - "metadata": {}, - "source": [ - "2\\. Create a retriever tool using LangChain's prebuilt `create_retriever_tool`:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "0b97bdd8-d7e3-444d-ac96-5ef4725f9048", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.tools.retriever import create_retriever_tool\n", - "\n", - "retriever_tool = create_retriever_tool(\n", - " retriever,\n", - " \"retrieve_blog_posts\",\n", - " \"Search and return information about Lilian Weng blog posts.\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "9cfdc027-5ea8-42ba-b0f4-0590505d76ff", - "metadata": {}, - "source": [ - "3\\. Test the tool:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "58c17a90-9a85-46c9-96a9-1e7b224330c4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'(Note: Some work defines reward tampering as a distinct category of misalignment behavior from reward hacking. But I consider reward hacking as a broader concept here.)\\nAt a high level, reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering.\\n\\nWhy does Reward Hacking Exist?#\\n\\nPan et al. (2022) investigated reward hacking as a function of agent capabilities, including (1) model size, (2) action space resolution, (3) observation space noise, and (4) training time. They also proposed a taxonomy of three types of misspecified proxy rewards:\\n\\nLet’s Define Reward Hacking#\\nReward shaping in RL is challenging. Reward hacking occurs when an RL agent exploits flaws or ambiguities in the reward function to obtain high rewards without genuinely learning the intended behaviors or completing the task as designed. In recent years, several related concepts have been proposed, all referring to some form of reward hacking:'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "retriever_tool.invoke({\"query\": \"types of reward hacking\"})" - ] - }, - { - "cell_type": "markdown", - "id": "7c4e9e70-3de1-4ab4-ab42-c298cef00eb6", - "metadata": {}, - "source": [ - "## 3. Generate query" - ] - }, - { - "cell_type": "markdown", - "id": "2ba89a91-3dc5-4663-a6a5-43ccb2575849", - "metadata": {}, - "source": [ - "Now we will start building components ([nodes](../../../concepts/low_level#nodes) and [edges](../../../concepts/low_level#edges)) for our agentic RAG graph. Note that the components will operate on the [`MessagesState`](../../../concepts/low_level#messagesstate) — graph state that contains a `messages` key with a list of [chat messages](https://python.langchain.com/docs/concepts/messages/).\n", - "\n", - "1\\. Build a `generate_query_or_respond` node. It will call an LLM to generate a response based on the current graph state (list of messages). Given the input messages, it will decide to retrieve using the retriever tool, or respond directly to the user. Note that we're giving the chat model access to the `retriever_tool` we created earlier via `.bind_tools`:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "32f21d6f-9d0e-44e1-8edd-1fd903164d6d", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.graph import MessagesState\n", - "from langchain.chat_models import init_chat_model\n", - "\n", - "response_model = init_chat_model(\"openai:gpt-4.1\", temperature=0)\n", - "\n", - "\n", - "def generate_query_or_respond(state: MessagesState):\n", - " \"\"\"Call the model to generate a response based on the current state. Given\n", - " the question, it will decide to retrieve using the retriever tool, or simply respond to the user.\n", - " \"\"\"\n", - " response = (\n", - " response_model\n", - " # highlight-next-line\n", - " .bind_tools([retriever_tool]).invoke(state[\"messages\"])\n", - " )\n", - " return {\"messages\": [response]}" - ] - }, - { - "cell_type": "markdown", - "id": "552e0f6c-5af5-4c13-91a6-437f320a4ed6", - "metadata": {}, - "source": [ - "2\\. Try it on a random input:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5cdc7e76-8680-42e6-9cdb-44aa4104f0f7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Hello! How can I help you today?\n" - ] - } - ], - "source": [ - "input = {\"messages\": [{\"role\": \"user\", \"content\": \"hello!\"}]}\n", - "generate_query_or_respond(input)[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "df45f7c8-df6c-4687-9f25-0357e29532cb", - "metadata": {}, - "source": [ - "3\\. Ask a question that requires semantic search:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "845e1bca-3097-423f-a9c8-967fe1216375", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " retrieve_blog_posts (call_tYQxgfIlnQUDMdtAhdbXNwIM)\n", - " Call ID: call_tYQxgfIlnQUDMdtAhdbXNwIM\n", - " Args:\n", - " query: types of reward hacking\n" - ] - } - ], - "source": [ - "input = {\n", - " \"messages\": [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"What does Lilian Weng say about types of reward hacking?\",\n", - " }\n", - " ]\n", - "}\n", - "generate_query_or_respond(input)[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "3e4756cb-b952-47a7-be4a-4f8a0866b67f", - "metadata": {}, - "source": [ - "## 4. Grade documents" - ] - }, - { - "cell_type": "markdown", - "id": "0b278967-18ab-409e-9ca4-6ee44a23e708", - "metadata": {}, - "source": [ - "1\\. Add a [conditional edge](../../../concepts/low_level#conditional-edges) — `grade_documents` — to determine whether the retrieved documents are relevant to the question. We will use a model with a structured output schema `GradeDocuments` for document grading. The `grade_documents` function will return the name of the node to go to based on the grading decision (`generate_answer` or `rewrite_question`):" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "98f0e1a5-5ca8-4d8c-a94c-fc0976ae1937", - "metadata": {}, - "outputs": [], - "source": [ - "from pydantic import BaseModel, Field\n", - "from typing import Literal\n", - "\n", - "GRADE_PROMPT = (\n", - " \"You are a grader assessing relevance of a retrieved document to a user question. \\n \"\n", - " \"Here is the retrieved document: \\n\\n {context} \\n\\n\"\n", - " \"Here is the user question: {question} \\n\"\n", - " \"If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \\n\"\n", - " \"Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\n", - ")\n", - "\n", - "\n", - "# highlight-next-line\n", - "class GradeDocuments(BaseModel):\n", - " \"\"\"Grade documents using a binary score for relevance check.\"\"\"\n", - "\n", - " binary_score: str = Field(\n", - " description=\"Relevance score: 'yes' if relevant, or 'no' if not relevant\"\n", - " )\n", - "\n", - "\n", - "grader_model = init_chat_model(\"openai:gpt-4.1\", temperature=0)\n", - "\n", - "\n", - "def grade_documents(\n", - " state: MessagesState,\n", - ") -> Literal[\"generate_answer\", \"rewrite_question\"]:\n", - " \"\"\"Determine whether the retrieved documents are relevant to the question.\"\"\"\n", - " question = state[\"messages\"][0].content\n", - " context = state[\"messages\"][-1].content\n", - "\n", - " prompt = GRADE_PROMPT.format(question=question, context=context)\n", - " response = (\n", - " grader_model\n", - " # highlight-next-line\n", - " .with_structured_output(GradeDocuments).invoke(\n", - " [{\"role\": \"user\", \"content\": prompt}]\n", - " )\n", - " )\n", - " score = response.binary_score\n", - "\n", - " if score == \"yes\":\n", - " return \"generate_answer\"\n", - " else:\n", - " return \"rewrite_question\"" - ] - }, - { - "cell_type": "markdown", - "id": "ddbdc0c2-6f0c-41ec-b4d1-ad20d4498006", - "metadata": {}, - "source": [ - "2\\. Run this with irrelevant documents in the tool response:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "0a62e5f7-4c4e-44cd-b4bd-77b6879ce970", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'rewrite_question'" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from langchain_core.messages import convert_to_messages\n", - "\n", - "input = {\n", - " \"messages\": convert_to_messages(\n", - " [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"What does Lilian Weng say about types of reward hacking?\",\n", - " },\n", - " {\n", - " \"role\": \"assistant\",\n", - " \"content\": \"\",\n", - " \"tool_calls\": [\n", - " {\n", - " \"id\": \"1\",\n", - " \"name\": \"retrieve_blog_posts\",\n", - " \"args\": {\"query\": \"types of reward hacking\"},\n", - " }\n", - " ],\n", - " },\n", - " {\"role\": \"tool\", \"content\": \"meow\", \"tool_call_id\": \"1\"},\n", - " ]\n", - " )\n", - "}\n", - "grade_documents(input)" - ] - }, - { - "cell_type": "markdown", - "id": "ba25a787-004e-4e6c-8f22-62087af7da1d", - "metadata": {}, - "source": [ - "3\\. Confirm that the relevant documents are classified as such:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "8bd5d24b-f05e-47c4-b954-267ab514a090", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'generate_answer'" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "input = {\n", - " \"messages\": convert_to_messages(\n", - " [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"What does Lilian Weng say about types of reward hacking?\",\n", - " },\n", - " {\n", - " \"role\": \"assistant\",\n", - " \"content\": \"\",\n", - " \"tool_calls\": [\n", - " {\n", - " \"id\": \"1\",\n", - " \"name\": \"retrieve_blog_posts\",\n", - " \"args\": {\"query\": \"types of reward hacking\"},\n", - " }\n", - " ],\n", - " },\n", - " {\n", - " \"role\": \"tool\",\n", - " \"content\": \"reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering\",\n", - " \"tool_call_id\": \"1\",\n", - " },\n", - " ]\n", - " )\n", - "}\n", - "grade_documents(input)" - ] - }, - { - "cell_type": "markdown", - "id": "4c337d45-bc35-4e88-8b27-ebc6631f9fed", - "metadata": {}, - "source": [ - "## 5. Rewrite question" - ] - }, - { - "cell_type": "markdown", - "id": "e8d67fdf-0b77-4b90-97f2-63a571c3e496", - "metadata": {}, - "source": [ - "1\\. Build the `rewrite_question` node. The retriever tool can return potentially irrelevant documents, which indicates a need to improve the original user question. To do so, we will call the `rewrite_question` node:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "278d1d83-dda6-4de4-bf8b-be9965c227fa", - "metadata": {}, - "outputs": [], - "source": [ - "REWRITE_PROMPT = (\n", - " \"Look at the input and try to reason about the underlying semantic intent / meaning.\\n\"\n", - " \"Here is the initial question:\"\n", - " \"\\n ------- \\n\"\n", - " \"{question}\"\n", - " \"\\n ------- \\n\"\n", - " \"Formulate an improved question:\"\n", - ")\n", - "\n", - "\n", - "def rewrite_question(state: MessagesState):\n", - " \"\"\"Rewrite the original user question.\"\"\"\n", - " messages = state[\"messages\"]\n", - " question = messages[0].content\n", - " prompt = REWRITE_PROMPT.format(question=question)\n", - " response = response_model.invoke([{\"role\": \"user\", \"content\": prompt}])\n", - " return {\"messages\": [{\"role\": \"user\", \"content\": response.content}]}" - ] - }, - { - "cell_type": "markdown", - "id": "1ec05f29-48a2-4325-8c56-da60490f2a60", - "metadata": {}, - "source": [ - "2\\. Try it out:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "15404075-b7c8-4248-84d3-ca2b361aa3d2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "What are the different types of reward hacking described by Lilian Weng, and how does she explain them?\n" - ] - } - ], - "source": [ - "input = {\n", - " \"messages\": convert_to_messages(\n", - " [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"What does Lilian Weng say about types of reward hacking?\",\n", - " },\n", - " {\n", - " \"role\": \"assistant\",\n", - " \"content\": \"\",\n", - " \"tool_calls\": [\n", - " {\n", - " \"id\": \"1\",\n", - " \"name\": \"retrieve_blog_posts\",\n", - " \"args\": {\"query\": \"types of reward hacking\"},\n", - " }\n", - " ],\n", - " },\n", - " {\"role\": \"tool\", \"content\": \"meow\", \"tool_call_id\": \"1\"},\n", - " ]\n", - " )\n", - "}\n", - "\n", - "response = rewrite_question(input)\n", - "print(response[\"messages\"][-1][\"content\"])" - ] - }, - { - "cell_type": "markdown", - "id": "55eab9fe-f1d6-4ad3-93d6-4638a92c9395", - "metadata": {}, - "source": [ - "## 6. Generate an answer" - ] - }, - { - "cell_type": "markdown", - "id": "d9362002-e66e-4e58-90af-d225c184b5d1", - "metadata": {}, - "source": [ - "1\\. Build `generate_answer` node: if we pass the grader checks, we can generate the final answer based on the original question and the retrieved context:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "20b0e60a-a5aa-4e95-a15a-2c91382e508f", - "metadata": {}, - "outputs": [], - "source": [ - "GENERATE_PROMPT = (\n", - " \"You are an assistant for question-answering tasks. \"\n", - " \"Use the following pieces of retrieved context to answer the question. \"\n", - " \"If you don't know the answer, just say that you don't know. \"\n", - " \"Use three sentences maximum and keep the answer concise.\\n\"\n", - " \"Question: {question} \\n\"\n", - " \"Context: {context}\"\n", - ")\n", - "\n", - "\n", - "def generate_answer(state: MessagesState):\n", - " \"\"\"Generate an answer.\"\"\"\n", - " question = state[\"messages\"][0].content\n", - " context = state[\"messages\"][-1].content\n", - " prompt = GENERATE_PROMPT.format(question=question, context=context)\n", - " response = response_model.invoke([{\"role\": \"user\", \"content\": prompt}])\n", - " return {\"messages\": [response]}" - ] - }, - { - "cell_type": "markdown", - "id": "8f59a1fc-fe75-4907-ae86-0dd33c4d9a7f", - "metadata": {}, - "source": [ - "2\\. Try it:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "15d84cb0-b529-49d0-b18e-1d03ff3f7c0d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Lilian Weng says that reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering. These categories describe different ways in which an agent might exploit flaws in the reward system. Environment or goal misspecification involves unintended behaviors due to poorly specified objectives, while reward tampering involves directly manipulating the reward signal.\n" - ] - } - ], - "source": [ - "input = {\n", - " \"messages\": convert_to_messages(\n", - " [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"What does Lilian Weng say about types of reward hacking?\",\n", - " },\n", - " {\n", - " \"role\": \"assistant\",\n", - " \"content\": \"\",\n", - " \"tool_calls\": [\n", - " {\n", - " \"id\": \"1\",\n", - " \"name\": \"retrieve_blog_posts\",\n", - " \"args\": {\"query\": \"types of reward hacking\"},\n", - " }\n", - " ],\n", - " },\n", - " {\n", - " \"role\": \"tool\",\n", - " \"content\": \"reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering\",\n", - " \"tool_call_id\": \"1\",\n", - " },\n", - " ]\n", - " )\n", - "}\n", - "\n", - "response = generate_answer(input)\n", - "response[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "955882ef-7467-48db-ae51-de441f2fc3a7", - "metadata": {}, - "source": [ - "## 7. Assemble the graph\n", - "\n", - "* Start with a `generate_query_or_respond` and determine if we need to call `retriever_tool`\n", - "* Route to next step using `tools_condition`:\n", - " * If `generate_query_or_respond` returned `tool_calls`, call `retriever_tool` to retrieve context \n", - " * Otherwise, respond directly to the user\n", - "* Grade retrieved document content for relevance to the question (`grade_documents`) and route to next step:\n", - " * If not relevant, rewrite the question using `rewrite_question` and then call `generate_query_or_respond` again\n", - " * If relevant, proceed to `generate_answer` and generate final response using the `ToolMessage` with the retrieved document context" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "8718a37f-83c2-4f16-9850-e61e0f49c3d4", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.graph import StateGraph, START, END\n", - "from langgraph.prebuilt import ToolNode\n", - "from langgraph.prebuilt import tools_condition\n", - "\n", - "workflow = StateGraph(MessagesState)\n", - "\n", - "# Define the nodes we will cycle between\n", - "workflow.add_node(generate_query_or_respond)\n", - "workflow.add_node(\"retrieve\", ToolNode([retriever_tool]))\n", - "workflow.add_node(rewrite_question)\n", - "workflow.add_node(generate_answer)\n", - "\n", - "workflow.add_edge(START, \"generate_query_or_respond\")\n", - "\n", - "# Decide whether to retrieve\n", - "workflow.add_conditional_edges(\n", - " \"generate_query_or_respond\",\n", - " # Assess LLM decision (call `retriever_tool` tool or respond to the user)\n", - " tools_condition,\n", - " {\n", - " # Translate the condition outputs to nodes in our graph\n", - " \"tools\": \"retrieve\",\n", - " END: END,\n", - " },\n", - ")\n", - "\n", - "# Edges taken after the `action` node is called.\n", - "workflow.add_conditional_edges(\n", - " \"retrieve\",\n", - " # Assess agent decision\n", - " grade_documents,\n", - ")\n", - "workflow.add_edge(\"generate_answer\", END)\n", - "workflow.add_edge(\"rewrite_question\", \"generate_query_or_respond\")\n", - "\n", - "# Compile\n", - "graph = workflow.compile()" - ] - }, - { - "cell_type": "markdown", - "id": "fb7c8138-8b01-4858-b9d1-b4bc5f7ebb33", - "metadata": {}, - "source": [ - "Visualize the graph:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "7b5a1d35", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "eb655cfb-d4e4-4f6a-8a0d-01e8611b18ab", - "metadata": {}, - "source": [ - "## 8. Run the agentic RAG" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "7649f05a-cb67-490d-b24a-74d41895139a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Update from node generate_query_or_respond\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " retrieve_blog_posts (call_NYu2vq4km9nNNEFqJwefWKu1)\n", - " Call ID: call_NYu2vq4km9nNNEFqJwefWKu1\n", - " Args:\n", - " query: types of reward hacking\n", - "\n", - "\n", - "\n", - "Update from node retrieve\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: retrieve_blog_posts\n", - "\n", - "(Note: Some work defines reward tampering as a distinct category of misalignment behavior from reward hacking. But I consider reward hacking as a broader concept here.)\n", - "At a high level, reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering.\n", - "\n", - "Why does Reward Hacking Exist?#\n", - "\n", - "Pan et al. (2022) investigated reward hacking as a function of agent capabilities, including (1) model size, (2) action space resolution, (3) observation space noise, and (4) training time. They also proposed a taxonomy of three types of misspecified proxy rewards:\n", - "\n", - "Let’s Define Reward Hacking#\n", - "Reward shaping in RL is challenging. Reward hacking occurs when an RL agent exploits flaws or ambiguities in the reward function to obtain high rewards without genuinely learning the intended behaviors or completing the task as designed. In recent years, several related concepts have been proposed, all referring to some form of reward hacking:\n", - "\n", - "\n", - "\n", - "Update from node generate_answer\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Lilian Weng categorizes reward hacking into two types: environment or goal misspecification, and reward tampering. She considers reward hacking as a broad concept that includes both of these categories. Reward hacking occurs when an agent exploits flaws or ambiguities in the reward function to achieve high rewards without performing the intended behaviors.\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "for chunk in graph.stream(\n", - " {\n", - " \"messages\": [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"What does Lilian Weng say about types of reward hacking?\",\n", - " }\n", - " ]\n", - " }\n", - "):\n", - " for node, update in chunk.items():\n", - " print(\"Update from node\", node)\n", - " update[\"messages\"][-1].pretty_print()\n", - " print(\"\\n\\n\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/docs/tutorials/rag/langgraph_agentic_rag.md b/docs/docs/tutorials/rag/langgraph_agentic_rag.md new file mode 100644 index 000000000..2aae24820 --- /dev/null +++ b/docs/docs/tutorials/rag/langgraph_agentic_rag.md @@ -0,0 +1,522 @@ +# Agentic RAG + +In this tutorial we will build a [retrieval agent](https://python.langchain.com/docs/tutorials/qa_chat_history). Retrieval agents are useful when you want an LLM to make a decision about whether to retrieve context from a vectorstore or respond to the user directly. + +By the end of the tutorial we will have done the following: + +1. Fetch and preprocess documents that will be used for retrieval. +2. Index those documents for semantic search and create a retriever tool for the agent. +3. Build an agentic RAG system that can decide when to use the retriever tool. + +![Screenshot 2024-02-14 at 3.43.58 PM.png](assets/screenshot_2024_02_14_3_43_58_pm.png) + +## Setup + +Let's download the required packages and set our API keys: + +```python +%%capture --no-stderr +%pip install -U --quiet langgraph "langchain[openai]" langchain-community langchain-text-splitters +``` + +```python +import getpass +import os + + +def _set_env(key: str): + if key not in os.environ: + os.environ[key] = getpass.getpass(f"{key}:") + + +_set_env("OPENAI_API_KEY") +``` + +!!! tip + Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. [LangSmith](https://docs.smith.langchain.com) lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph. + + +## 1. Preprocess documents + +1. Fetch documents to use in our RAG system. We will use three of the most recent pages from [Lilian Weng's excellent blog](https://lilianweng.github.io/). We'll start by fetching the content of the pages using `WebBaseLoader` utility: + + ```python + from langchain_community.document_loaders import WebBaseLoader + + urls = [ + "https://lilianweng.github.io/posts/2024-11-28-reward-hacking/", + "https://lilianweng.github.io/posts/2024-07-07-hallucination/", + "https://lilianweng.github.io/posts/2024-04-12-diffusion-video/", + ] + + docs = [WebBaseLoader(url).load() for url in urls] + ``` + + ```python + docs[0][0].page_content.strip()[:1000] + ``` + +2. Split the fetched documents into smaller chunks for indexing into our vectorstore: + + ```python + from langchain_text_splitters import RecursiveCharacterTextSplitter + + docs_list = [item for sublist in docs for item in sublist] + + text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder( + chunk_size=100, chunk_overlap=50 + ) + doc_splits = text_splitter.split_documents(docs_list) + ``` + + ```python + doc_splits[0].page_content.strip() + ``` + +## 2. Create a retriever tool + +Now that we have our split documents, we can index them into a vector store that we'll use for semantic search. + +1. Use an in-memory vector store and OpenAI embeddings: + + ```python + from langchain_core.vectorstores import InMemoryVectorStore + from langchain_openai import OpenAIEmbeddings + + vectorstore = InMemoryVectorStore.from_documents( + documents=doc_splits, embedding=OpenAIEmbeddings() + ) + retriever = vectorstore.as_retriever() + ``` + +2. Create a retriever tool using LangChain's prebuilt `create_retriever_tool`: + + ```python + from langchain.tools.retriever import create_retriever_tool + + retriever_tool = create_retriever_tool( + retriever, + "retrieve_blog_posts", + "Search and return information about Lilian Weng blog posts.", + ) + ``` + +3. Test the tool: + + ```python + retriever_tool.invoke({"query": "types of reward hacking"}) + ``` + +## 3. Generate query + +Now we will start building components ([nodes](../../concepts/low_level.md#nodes) and [edges](../../concepts/low_level.md#edges)) for our agentic RAG graph. Note that the components will operate on the [`MessagesState`](../../concepts/low_level.md#messagesstate) — graph state that contains a `messages` key with a list of [chat messages](https://python.langchain.com/docs/concepts/messages/). + +1. Build a `generate_query_or_respond` node. It will call an LLM to generate a response based on the current graph state (list of messages). Given the input messages, it will decide to retrieve using the retriever tool, or respond directly to the user. Note that we're giving the chat model access to the `retriever_tool` we created earlier via `.bind_tools`: + + ```python + from langgraph.graph import MessagesState + from langchain.chat_models import init_chat_model + + response_model = init_chat_model("openai:gpt-4.1", temperature=0) + + + def generate_query_or_respond(state: MessagesState): + """Call the model to generate a response based on the current state. Given + the question, it will decide to retrieve using the retriever tool, or simply respond to the user. + """ + response = ( + response_model + # highlight-next-line + .bind_tools([retriever_tool]).invoke(state["messages"]) + ) + return {"messages": [response]} + ``` + +2. Try it on a random input: + + ```python + input = {"messages": [{"role": "user", "content": "hello!"}]} + generate_query_or_respond(input)["messages"][-1].pretty_print() + ``` + + **Output:** + ``` + ================================== Ai Message ================================== + + Hello! How can I help you today? + ``` + +3. Ask a question that requires semantic search: + + ```python + input = { + "messages": [ + { + "role": "user", + "content": "What does Lilian Weng say about types of reward hacking?", + } + ] + } + generate_query_or_respond(input)["messages"][-1].pretty_print() + ``` + + **Output:** + ``` + ================================== Ai Message ================================== + Tool Calls: + retrieve_blog_posts (call_tYQxgfIlnQUDMdtAhdbXNwIM) + Call ID: call_tYQxgfIlnQUDMdtAhdbXNwIM + Args: + query: types of reward hacking + ``` + +## 4. Grade documents + +1. Add a [conditional edge](../../concepts/low_level.md#conditional-edges) — `grade_documents` — to determine whether the retrieved documents are relevant to the question. We will use a model with a structured output schema `GradeDocuments` for document grading. The `grade_documents` function will return the name of the node to go to based on the grading decision (`generate_answer` or `rewrite_question`): + + ```python + from pydantic import BaseModel, Field + from typing import Literal + + GRADE_PROMPT = ( + "You are a grader assessing relevance of a retrieved document to a user question. \n " + "Here is the retrieved document: \n\n {context} \n\n" + "Here is the user question: {question} \n" + "If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \n" + "Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question." + ) + + + # highlight-next-line + class GradeDocuments(BaseModel): + """Grade documents using a binary score for relevance check.""" + + binary_score: str = Field( + description="Relevance score: 'yes' if relevant, or 'no' if not relevant" + ) + + + grader_model = init_chat_model("openai:gpt-4.1", temperature=0) + + + def grade_documents( + state: MessagesState, + ) -> Literal["generate_answer", "rewrite_question"]: + """Determine whether the retrieved documents are relevant to the question.""" + question = state["messages"][0].content + context = state["messages"][-1].content + + prompt = GRADE_PROMPT.format(question=question, context=context) + response = ( + grader_model + # highlight-next-line + .with_structured_output(GradeDocuments).invoke( + [{"role": "user", "content": prompt}] + ) + ) + score = response.binary_score + + if score == "yes": + return "generate_answer" + else: + return "rewrite_question" + ``` + +2. Run this with irrelevant documents in the tool response: + + ```python + from langchain_core.messages import convert_to_messages + + input = { + "messages": convert_to_messages( + [ + { + "role": "user", + "content": "What does Lilian Weng say about types of reward hacking?", + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "id": "1", + "name": "retrieve_blog_posts", + "args": {"query": "types of reward hacking"}, + } + ], + }, + {"role": "tool", "content": "meow", "tool_call_id": "1"}, + ] + ) + } + grade_documents(input) + ``` + +3. Confirm that the relevant documents are classified as such: + + ```python + input = { + "messages": convert_to_messages( + [ + { + "role": "user", + "content": "What does Lilian Weng say about types of reward hacking?", + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "id": "1", + "name": "retrieve_blog_posts", + "args": {"query": "types of reward hacking"}, + } + ], + }, + { + "role": "tool", + "content": "reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering", + "tool_call_id": "1", + }, + ] + ) + } + grade_documents(input) + ``` + +## 5. Rewrite question + +1. Build the `rewrite_question` node. The retriever tool can return potentially irrelevant documents, which indicates a need to improve the original user question. To do so, we will call the `rewrite_question` node: + + ```python + REWRITE_PROMPT = ( + "Look at the input and try to reason about the underlying semantic intent / meaning.\n" + "Here is the initial question:" + "\n ------- \n" + "{question}" + "\n ------- \n" + "Formulate an improved question:" + ) + + + def rewrite_question(state: MessagesState): + """Rewrite the original user question.""" + messages = state["messages"] + question = messages[0].content + prompt = REWRITE_PROMPT.format(question=question) + response = response_model.invoke([{"role": "user", "content": prompt}]) + return {"messages": [{"role": "user", "content": response.content}]} + ``` + +2. Try it out: + + ```python + input = { + "messages": convert_to_messages( + [ + { + "role": "user", + "content": "What does Lilian Weng say about types of reward hacking?", + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "id": "1", + "name": "retrieve_blog_posts", + "args": {"query": "types of reward hacking"}, + } + ], + }, + {"role": "tool", "content": "meow", "tool_call_id": "1"}, + ] + ) + } + + response = rewrite_question(input) + print(response["messages"][-1]["content"]) + ``` + + **Output:** + ``` + What are the different types of reward hacking described by Lilian Weng, and how does she explain them? + ``` + +## 6. Generate an answer + +1. Build `generate_answer` node: if we pass the grader checks, we can generate the final answer based on the original question and the retrieved context: + + ```python + GENERATE_PROMPT = ( + "You are an assistant for question-answering tasks. " + "Use the following pieces of retrieved context to answer the question. " + "If you don't know the answer, just say that you don't know. " + "Use three sentences maximum and keep the answer concise.\n" + "Question: {question} \n" + "Context: {context}" + ) + + + def generate_answer(state: MessagesState): + """Generate an answer.""" + question = state["messages"][0].content + context = state["messages"][-1].content + prompt = GENERATE_PROMPT.format(question=question, context=context) + response = response_model.invoke([{"role": "user", "content": prompt}]) + return {"messages": [response]} + ``` + +2. Try it: + + ```python + input = { + "messages": convert_to_messages( + [ + { + "role": "user", + "content": "What does Lilian Weng say about types of reward hacking?", + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "id": "1", + "name": "retrieve_blog_posts", + "args": {"query": "types of reward hacking"}, + } + ], + }, + { + "role": "tool", + "content": "reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering", + "tool_call_id": "1", + }, + ] + ) + } + + response = generate_answer(input) + response["messages"][-1].pretty_print() + ``` + + **Output:** + ``` + ================================== Ai Message ================================== + + Lilian Weng categorizes reward hacking into two types: environment or goal misspecification, and reward tampering. She considers reward hacking as a broad concept that includes both of these categories. Reward hacking occurs when an agent exploits flaws or ambiguities in the reward function to achieve high rewards without performing the intended behaviors. + ``` + +## 7. Assemble the graph + +* Start with a `generate_query_or_respond` and determine if we need to call `retriever_tool` +* Route to next step using `tools_condition`: + * If `generate_query_or_respond` returned `tool_calls`, call `retriever_tool` to retrieve context + * Otherwise, respond directly to the user +* Grade retrieved document content for relevance to the question (`grade_documents`) and route to next step: + * If not relevant, rewrite the question using `rewrite_question` and then call `generate_query_or_respond` again + * If relevant, proceed to `generate_answer` and generate final response using the `ToolMessage` with the retrieved document context + +```python +from langgraph.graph import StateGraph, START, END +from langgraph.prebuilt import ToolNode +from langgraph.prebuilt import tools_condition + +workflow = StateGraph(MessagesState) + +# Define the nodes we will cycle between +workflow.add_node(generate_query_or_respond) +workflow.add_node("retrieve", ToolNode([retriever_tool])) +workflow.add_node(rewrite_question) +workflow.add_node(generate_answer) + +workflow.add_edge(START, "generate_query_or_respond") + +# Decide whether to retrieve +workflow.add_conditional_edges( + "generate_query_or_respond", + # Assess LLM decision (call `retriever_tool` tool or respond to the user) + tools_condition, + { + # Translate the condition outputs to nodes in our graph + "tools": "retrieve", + END: END, + }, +) + +# Edges taken after the `action` node is called. +workflow.add_conditional_edges( + "retrieve", + # Assess agent decision + grade_documents, +) +workflow.add_edge("generate_answer", END) +workflow.add_edge("rewrite_question", "generate_query_or_respond") + +# Compile +graph = workflow.compile() +``` + +Visualize the graph: + +```python +from IPython.display import Image, display + +display(Image(graph.get_graph().draw_mermaid_png())) +``` + +![Graph](assets/agentic-rag-output.png) + +## 8. Run the agentic RAG + +```python +for chunk in graph.stream( + { + "messages": [ + { + "role": "user", + "content": "What does Lilian Weng say about types of reward hacking?", + } + ] + } +): + for node, update in chunk.items(): + print("Update from node", node) + update["messages"][-1].pretty_print() + print("\n\n") +``` + +**Output:** +``` +Update from node generate_query_or_respond +================================== Ai Message ================================== +Tool Calls: + retrieve_blog_posts (call_NYu2vq4km9nNNEFqJwefWKu1) + Call ID: call_NYu2vq4km9nNNEFqJwefWKu1 + Args: + query: types of reward hacking + + + +Update from node retrieve +================================= Tool Message ================================== +Name: retrieve_blog_posts + +(Note: Some work defines reward tampering as a distinct category of misalignment behavior from reward hacking. But I consider reward hacking as a broader concept here.) +At a high level, reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering. + +Why does Reward Hacking Exist?# + +Pan et al. (2022) investigated reward hacking as a function of agent capabilities, including (1) model size, (2) action space resolution, (3) observation space noise, and (4) training time. They also proposed a taxonomy of three types of misspecified proxy rewards: + +Let's Define Reward Hacking# +Reward shaping in RL is challenging. Reward hacking occurs when an RL agent exploits flaws or ambiguities in the reward function to obtain high rewards without genuinely learning the intended behaviors or completing the task as designed. In recent years, several related concepts have been proposed, all referring to some form of reward hacking: + + + +Update from node generate_answer +================================== Ai Message ================================== + +Lilian Weng categorizes reward hacking into two types: environment or goal misspecification, and reward tampering. She considers reward hacking as a broad concept that includes both of these categories. Reward hacking occurs when an agent exploits flaws or ambiguities in the reward function to achieve high rewards without performing the intended behaviors. +``` \ No newline at end of file diff --git a/docs/docs/tutorials/sql/output.png b/docs/docs/tutorials/sql/output.png new file mode 100644 index 0000000000000000000000000000000000000000..006d100aad6a03be8929fe5af5806217e83f79e7 GIT binary patch literal 20460 zcmcG$byOT}m-ZWZ5(q&O91| z0l(>o{qTG8GWf)Ce2=M)kQv4mGYb>3yaS^EQ{B38m+>}FC|AuM)02;iS<1B zE`?0_B=?Q%6IoK|6R04^Qw~4tXVxgozTwzsOjLXj96UIva3l&7L~x1is#_+k|BZ~5 zF>Z>3hTu{df76%8<<710u#Mwp6SVL>Fu}WQYz&eDO*5QR+>%)E|CHODoR!t+`u#JIa%j(~Ya-uN>=3AhaU$q(Ds(qC+Qm(-+Z!T8^phh1@O=bG= zz23-Pl`g3SHe0^VP<2IAM~J)78S_mbR+lkK#1MqvcI8FGkN;Zs#c;O&m^3h7<+Fbl zN?w#886?Ns$o)G#q{&D~u@lV~%l72Xl9qKa>8w(hYw0AC(N5jPZ>BQ@1DR5PcX0Hr zLVAIrhdo*1k41DpN&O60B02)3$_53D7aJY-HA9mUT(Qz4rDlQc9OtY283aggWPd-y zNmyXdoJFlNsLNHe+GQWa6kPmNmf>$Lh)YlM+M%RdYP9f3DNoOKTl_PPDKcdP%@BKJ 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Fetch the available tables from the database +2. Decide which tables are relevant to the question +3. Fetch the schemas for the relevant tables +4. Generate a query based on the question and information from the schemas +5. Double-check the query for common mistakes using an LLM +6. Execute the query and return the results +7. Correct mistakes surfaced by the database engine until the query is successful +8. Formulate a response based on the results + +!!! warning "Security note" + Building Q&A systems of SQL databases requires executing model-generated SQL queries. There are inherent risks in doing this. Make sure that your database connection permissions are always scoped as narrowly as possible for your agent's needs. This will mitigate though not eliminate the risks of building a model-driven system. + +## 1. Setup + +Let's first install some dependencies. This tutorial uses SQL database and tool abstractions from [langchain-community](https://python.langchain.com/docs/concepts/architecture/#langchain-community). We will also require a LangChain [chat model](https://python.langchain.com/docs/concepts/chat_models/). + +```python +%%capture --no-stderr +%pip install -U langgraph langchain_community "langchain[openai]" +``` + +!!! tip + Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. [LangSmith](https://docs.smith.langchain.com) lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph. + +### Select a LLM + +First we [initialize our LLM](https://python.langchain.com/docs/how_to/chat_models_universal_init/). Any model supporting [tool-calling](https://python.langchain.com/docs/integrations/chat/#featured-providers) should work. We use OpenAI below. + +```python +from langchain.chat_models import init_chat_model + +llm = init_chat_model("openai:gpt-4.1") +``` + +### Configure the database + +We will be creating a SQLite database for this tutorial. SQLite is a lightweight database that is easy to set up and use. We will be loading the `chinook` database, which is a sample database that represents a digital media store. +Find more information about the database [here](https://www.sqlitetutorial.net/sqlite-sample-database/). + +For convenience, we have hosted the database (`Chinook.db`) on a public GCS bucket. + +```python +import requests + +url = "https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db" + +response = requests.get(url) + +if response.status_code == 200: + # Open a local file in binary write mode + with open("Chinook.db", "wb") as file: + # Write the content of the response (the file) to the local file + file.write(response.content) + print("File downloaded and saved as Chinook.db") +else: + print(f"Failed to download the file. Status code: {response.status_code}") +``` + +We will use a handy SQL database wrapper available in the `langchain_community` package to interact with the database. The wrapper provides a simple interface to execute SQL queries and fetch results: + +```python +from langchain_community.utilities import SQLDatabase + +db = SQLDatabase.from_uri("sqlite:///Chinook.db") + +print(f"Dialect: {db.dialect}") +print(f"Available tables: {db.get_usable_table_names()}") +print(f'Sample output: {db.run("SELECT * FROM Artist LIMIT 5;")}') +``` + +**Output:** +``` +Dialect: sqlite +Available tables: ['Album', 'Artist', 'Customer', 'Employee', 'Genre', 'Invoice', 'InvoiceLine', 'MediaType', 'Playlist', 'PlaylistTrack', 'Track'] +Sample output: [(1, 'AC/DC'), (2, 'Accept'), (3, 'Aerosmith'), (4, 'Alanis Morissette'), (5, 'Alice In Chains')] +``` + +### Tools for database interactions + +`langchain-community` implements some built-in tools for interacting with our `SQLDatabase`, including tools for listing tables, reading table schemas, and checking and running queries: + +```python +from langchain_community.agent_toolkits import SQLDatabaseToolkit + +toolkit = SQLDatabaseToolkit(db=db, llm=llm) + +tools = toolkit.get_tools() + +for tool in tools: + print(f"{tool.name}: {tool.description}\n") +``` + +**Output:** +``` +sql_db_query: Input to this tool is a detailed and correct SQL query, output is a result from the database. If the query is not correct, an error message will be returned. If an error is returned, rewrite the query, check the query, and try again. If you encounter an issue with Unknown column 'xxxx' in 'field list', use sql_db_schema to query the correct table fields. + +sql_db_schema: Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. Be sure that the tables actually exist by calling sql_db_list_tables first! Example Input: table1, table2, table3 + +sql_db_list_tables: Input is an empty string, output is a comma-separated list of tables in the database. + +sql_db_query_checker: Use this tool to double check if your query is correct before executing it. Always use this tool before executing a query with sql_db_query! + +``` + +## 2. Using a prebuilt agent + +Given these tools, we can initialize a pre-built agent in a single line. To customize our agents behavior, we write a descriptive system prompt. + +```python +from langgraph.prebuilt import create_react_agent + +system_prompt = """ +You are an agent designed to interact with a SQL database. +Given an input question, create a syntactically correct {dialect} query to run, +then look at the results of the query and return the answer. Unless the user +specifies a specific number of examples they wish to obtain, always limit your +query to at most {top_k} results. + +You can order the results by a relevant column to return the most interesting +examples in the database. Never query for all the columns from a specific table, +only ask for the relevant columns given the question. + +You MUST double check your query before executing it. If you get an error while +executing a query, rewrite the query and try again. + +DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the +database. + +To start you should ALWAYS look at the tables in the database to see what you +can query. Do NOT skip this step. + +Then you should query the schema of the most relevant tables. +""".format( + dialect=db.dialect, + top_k=5, +) + +agent = create_react_agent( + llm, + tools, + prompt=system_prompt, +) +``` + +!!! note + This system prompt includes a number of instructions, such as always running specific tools before or after others. In the [next section](#3-customizing-the-agent), we will enforce these behaviors through the graph's structure, providing us a greater degree of control and allowing us to simplify the prompt. + +Let's run this agent on a sample query and observe its behavior: + +```python +question = "Which genre on average has the longest tracks?" + +for step in agent.stream( + {"messages": [{"role": "user", "content": question}]}, + stream_mode="values", +): + step["messages"][-1].pretty_print() +``` + +**Output:** +``` +================================ Human Message ================================= + +Which genre on average has the longest tracks? +================================== Ai Message ================================== +Tool Calls: + sql_db_list_tables (call_d8lCgywSroCgpVl558nmXKwA) + Call ID: call_d8lCgywSroCgpVl558nmXKwA + Args: +================================= Tool Message ================================= +Name: sql_db_list_tables + +Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track +================================== Ai Message ================================== +Tool Calls: + sql_db_schema (call_nNf6IIUcwMYLIkE0l6uWkZHe) + Call ID: call_nNf6IIUcwMYLIkE0l6uWkZHe + Args: + table_names: Genre, Track +================================= Tool Message ================================= +Name: sql_db_schema + + +CREATE TABLE "Genre" ( + "GenreId" INTEGER NOT NULL, + "Name" NVARCHAR(120), + PRIMARY KEY ("GenreId") +) + +/* +3 rows from Genre table: +GenreId Name +1 Rock +2 Jazz +3 Metal +*/ + + +CREATE TABLE "Track" ( + "TrackId" INTEGER NOT NULL, + "Name" NVARCHAR(200) NOT NULL, + "AlbumId" INTEGER, + "MediaTypeId" INTEGER NOT NULL, + "GenreId" INTEGER, + "Composer" NVARCHAR(220), + "Milliseconds" INTEGER NOT NULL, + "Bytes" INTEGER, + "UnitPrice" NUMERIC(10, 2) NOT NULL, + PRIMARY KEY ("TrackId"), + FOREIGN KEY("MediaTypeId") REFERENCES "MediaType" ("MediaTypeId"), + FOREIGN KEY("GenreId") REFERENCES "Genre" ("GenreId"), + FOREIGN KEY("AlbumId") REFERENCES "Album" ("AlbumId") +) + +/* +3 rows from Track table: +TrackId Name AlbumId MediaTypeId GenreId Composer Milliseconds Bytes UnitPrice +1 For Those About To Rock (We Salute You) 1 1 1 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 0.99 +2 Balls to the Wall 2 2 1 None 342562 5510424 0.99 +3 Fast As a Shark 3 2 1 F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman 230619 3990994 0.99 +*/ +================================== Ai Message ================================== +Tool Calls: + sql_db_query_checker (call_urTRmtiGtTxkwHtscec7Fd2K) + Call ID: call_urTRmtiGtTxkwHtscec7Fd2K + Args: + query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgMilliseconds +FROM Track +JOIN Genre ON Track.GenreId = Genre.GenreId +GROUP BY Genre.Name +ORDER BY AvgMilliseconds DESC +LIMIT 1; +================================= Tool Message ================================= +Name: sql_db_query_checker + +\`\`\`sql +SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgMilliseconds +FROM Track +JOIN Genre ON Track.GenreId = Genre.GenreId +GROUP BY Genre.Name +ORDER BY AvgMilliseconds DESC +LIMIT 1; +\`\`\` +================================== Ai Message ================================== +Tool Calls: + sql_db_query (call_RNMqyUEMv0rvy0UxSwrXY2AV) + Call ID: call_RNMqyUEMv0rvy0UxSwrXY2AV + Args: + query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgMilliseconds +FROM Track +JOIN Genre ON Track.GenreId = Genre.GenreId +GROUP BY Genre.Name +ORDER BY AvgMilliseconds DESC +LIMIT 1; +================================= Tool Message ================================= +Name: sql_db_query + +[('Sci Fi & Fantasy', 2911783.0384615385)] +================================== Ai Message ================================== + +The genre with the longest average track length is "Sci Fi & Fantasy," with an average duration of about 2,911,783 milliseconds (approximately 48.5 minutes) per track. +``` + +This worked well enough: the agent correctly listed the tables, obtained the schemas, wrote a query, checked the query, and ran it to inform its final response. + +!!! tip + You can inspect all aspects of the above run, including steps taken, tools invoked, what prompts were seen by the LLM, and more in the [LangSmith trace](https://smith.langchain.com/public/bd594960-73e3-474b-b6f2-db039d7c713a/r). + +## 3. Customizing the agent + +The prebuilt agent lets us get started quickly, but at each step the agent has access to the full set of tools. Above, we relied on the system prompt to constrain its behavior— for example, we instructed the agent to always start with the "list tables" tool, and to always run a query-checker tool before executing the query. + +We can enforce a higher degree of control in LangGraph by customizing the agent. Below, we implement a simple ReAct-agent setup, with dedicated nodes for specific tool-calls. We will use the same [state](../../concepts/low_level.md#state) as the pre-built agent. + +We construct dedicated nodes for the following steps: + +- Listing DB tables +- Calling the "get schema" tool +- Generating a query +- Checking the query + +Putting these steps in dedicated nodes lets us (1) force tool-calls when needed, and (2) customize the prompts associated with each step. + +```python +from typing import Literal +from langchain_core.messages import AIMessage +from langchain_core.runnables import RunnableConfig +from langgraph.graph import END, START, MessagesState, StateGraph +from langgraph.prebuilt import ToolNode + + +get_schema_tool = next(tool for tool in tools if tool.name == "sql_db_schema") +get_schema_node = ToolNode([get_schema_tool], name="get_schema") + +run_query_tool = next(tool for tool in tools if tool.name == "sql_db_query") +run_query_node = ToolNode([run_query_tool], name="run_query") + + +# Example: create a predetermined tool call +def list_tables(state: MessagesState): + tool_call = { + "name": "sql_db_list_tables", + "args": {}, + "id": "abc123", + "type": "tool_call", + } + tool_call_message = AIMessage(content="", tool_calls=[tool_call]) + + list_tables_tool = next(tool for tool in tools if tool.name == "sql_db_list_tables") + tool_message = list_tables_tool.invoke(tool_call) + response = AIMessage(f"Available tables: {tool_message.content}") + + return {"messages": [tool_call_message, tool_message, response]} + + +# Example: force a model to create a tool call +def call_get_schema(state: MessagesState): + # Note that LangChain enforces that all models accept `tool_choice="any"` + # as well as `tool_choice=`. + llm_with_tools = llm.bind_tools([get_schema_tool], tool_choice="any") + response = llm_with_tools.invoke(state["messages"]) + + return {"messages": [response]} + + +generate_query_system_prompt = """ +You are an agent designed to interact with a SQL database. +Given an input question, create a syntactically correct {dialect} query to run, +then look at the results of the query and return the answer. Unless the user +specifies a specific number of examples they wish to obtain, always limit your +query to at most {top_k} results. + +You can order the results by a relevant column to return the most interesting +examples in the database. Never query for all the columns from a specific table, +only ask for the relevant columns given the question. + +DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database. +""".format( + dialect=db.dialect, + top_k=5, +) + + +def generate_query(state: MessagesState): + system_message = { + "role": "system", + "content": generate_query_system_prompt, + } + # We do not force a tool call here, to allow the model to + # respond naturally when it obtains the solution. + llm_with_tools = llm.bind_tools([run_query_tool]) + response = llm_with_tools.invoke([system_message] + state["messages"]) + + return {"messages": [response]} + + +check_query_system_prompt = """ +You are a SQL expert with a strong attention to detail. +Double check the {dialect} query for common mistakes, including: +- Using NOT IN with NULL values +- Using UNION when UNION ALL should have been used +- Using BETWEEN for exclusive ranges +- Data type mismatch in predicates +- Properly quoting identifiers +- Using the correct number of arguments for functions +- Casting to the correct data type +- Using the proper columns for joins + +If there are any of the above mistakes, rewrite the query. If there are no mistakes, +just reproduce the original query. + +You will call the appropriate tool to execute the query after running this check. +""".format(dialect=db.dialect) + + +def check_query(state: MessagesState): + system_message = { + "role": "system", + "content": check_query_system_prompt, + } + + # Generate an artificial user message to check + tool_call = state["messages"][-1].tool_calls[0] + user_message = {"role": "user", "content": tool_call["args"]["query"]} + llm_with_tools = llm.bind_tools([run_query_tool], tool_choice="any") + response = llm_with_tools.invoke([system_message, user_message]) + response.id = state["messages"][-1].id + + return {"messages": [response]} +``` + +Finally, we assemble these steps into a workflow using the Graph API. We define a [conditional edge](../../concepts/low_level.md#conditional-edges) at the query generation step that will route to the query checker if a query is generated, or end if there are no tool calls present, such that the LLM has delivered a response to the query. + +```python +def should_continue(state: MessagesState) -> Literal[END, "check_query"]: + messages = state["messages"] + last_message = messages[-1] + if not last_message.tool_calls: + return END + else: + return "check_query" + + +builder = StateGraph(MessagesState) +builder.add_node(list_tables) +builder.add_node(call_get_schema) +builder.add_node(get_schema_node, "get_schema") +builder.add_node(generate_query) +builder.add_node(check_query) +builder.add_node(run_query_node, "run_query") + +builder.add_edge(START, "list_tables") +builder.add_edge("list_tables", "call_get_schema") +builder.add_edge("call_get_schema", "get_schema") +builder.add_edge("get_schema", "generate_query") +builder.add_conditional_edges( + "generate_query", + should_continue, +) +builder.add_edge("check_query", "run_query") +builder.add_edge("run_query", "generate_query") + +agent = builder.compile() +``` + +We visualize the application below: + +```python +from IPython.display import Image, display +from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeStyles + +display(Image(agent.get_graph().draw_mermaid_png())) +``` + +![Graph](./output.png) + +**Note:** When you run this code, it will generate and display a visual representation of the SQL agent graph showing the flow between the different nodes (list_tables → call_get_schema → get_schema → generate_query → check_query → run_query). + +We can now invoke the graph exactly as before: + +```python +question = "Which genre on average has the longest tracks?" + +for step in agent.stream( + {"messages": [{"role": "user", "content": question}]}, + stream_mode="values", +): + step["messages"][-1].pretty_print() +``` + +**Output:** +``` +================================ Human Message ================================= + +Which genre on average has the longest tracks? +================================== Ai Message ================================== + +Available tables: Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track +================================== Ai Message ================================== +Tool Calls: + sql_db_schema (call_qxKtYiHgf93AiTDin9ez5wFp) + Call ID: call_qxKtYiHgf93AiTDin9ez5wFp + Args: + table_names: Genre,Track +================================= Tool Message ================================= +Name: sql_db_schema + + +CREATE TABLE "Genre" ( + "GenreId" INTEGER NOT NULL, + "Name" NVARCHAR(120), + PRIMARY KEY ("GenreId") +) + +/* +3 rows from Genre table: +GenreId Name +1 Rock +2 Jazz +3 Metal +*/ + + +CREATE TABLE "Track" ( + "TrackId" INTEGER NOT NULL, + "Name" NVARCHAR(200) NOT NULL, + "AlbumId" INTEGER, + "MediaTypeId" INTEGER NOT NULL, + "GenreId" INTEGER, + "Composer" NVARCHAR(220), + "Milliseconds" INTEGER NOT NULL, + "Bytes" INTEGER, + "UnitPrice" NUMERIC(10, 2) NOT NULL, + PRIMARY KEY ("TrackId"), + FOREIGN KEY("MediaTypeId") REFERENCES "MediaType" ("MediaTypeId"), + FOREIGN KEY("GenreId") REFERENCES "Genre" ("GenreId"), + FOREIGN KEY("AlbumId") REFERENCES "Album" ("AlbumId") +) + +/* +3 rows from Track table: +TrackId Name AlbumId MediaTypeId GenreId Composer Milliseconds Bytes UnitPrice +1 For Those About To Rock (We Salute You) 1 1 1 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 0.99 +2 Balls to the Wall 2 2 1 None 342562 5510424 0.99 +3 Fast As a Shark 3 2 1 F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman 230619 3990994 0.99 +*/ +================================== Ai Message ================================== +Tool Calls: + sql_db_query (call_RPN3GABMfb6DTaFTLlwnZxVN) + Call ID: call_RPN3GABMfb6DTaFTLlwnZxVN + Args: + query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgTrackLength +FROM Track +JOIN Genre ON Track.GenreId = Genre.GenreId +GROUP BY Genre.GenreId +ORDER BY AvgTrackLength DESC +LIMIT 1; +================================== Ai Message ================================== +Tool Calls: + sql_db_query (call_PR4s8ymiF3ZQLaoZADXtdqcl) + Call ID: call_PR4s8ymiF3ZQLaoZADXtdqcl + Args: + query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgTrackLength +FROM Track +JOIN Genre ON Track.GenreId = Genre.GenreId +GROUP BY Genre.GenreId +ORDER BY AvgTrackLength DESC +LIMIT 1; +================================= Tool Message ================================= +Name: sql_db_query + +[('Sci Fi & Fantasy', 2911783.0384615385)] +================================== Ai Message ================================== + +The genre with the longest tracks on average is "Sci Fi & Fantasy," with an average track length of approximately 2,911,783 milliseconds. +``` + +!!! tip + See [LangSmith trace](https://smith.langchain.com/public/94b8c9ac-12f7-4692-8706-836a1f30f1ea/r) for the above run. + +## Next steps + +Check out [this guide](https://docs.smith.langchain.com/evaluation/how_to_guides/langgraph) for evaluating LangGraph applications, including SQL agents like this one, using LangSmith. \ No newline at end of file diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 7cfbb246f..020c6e28a 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -269,19 +269,18 @@ nav: - Examples: - Template applications: concepts/template_applications.md # TODO: make tutorial - - Agentic RAG: tutorials/rag/langgraph_agentic_rag.ipynb - - Agent Supervisor: tutorials/multi_agent/agent_supervisor.ipynb - - SQL agent: tutorials/sql-agent.ipynb + - Agentic RAG: tutorials/rag/langgraph_agentic_rag.md + - Agent Supervisor: tutorials/multi_agent/agent_supervisor.md + - SQL agent: tutorials/sql/sql-agent.md - Prebuilt chat UI: agents/ui.md - - Graph runs in LangSmith: how-tos/run-id-langsmith.ipynb + - Graph runs in LangSmith: how-tos/run-id-langsmith.md - LangGraph Platform: - Authentication: - tutorials/auth/getting_started.md - tutorials/auth/resource_auth.md - tutorials/auth/add_auth_server.md - Use RemoteGraph: how-tos/use-remote-graph.md - - Deploy CrewAI, AutoGen, and other frameworks: how-tos/autogen-langgraph-platform.ipynb - # combine with how-tos/autogen-integration.ipynb + - Deploy CrewAI, AutoGen, and other frameworks: how-tos/autogen-integration.md - Front-end and generative UI: - Integrate LangGraph into a React app: cloud/how-tos/use_stream_react.md - Implement generative UI with LangGraph: cloud/how-tos/generative_ui_react.md From 4de1bd6e666ba9305d259f8f043608c4f9c6aac9 Mon Sep 17 00:00:00 2001 From: Lauren Hirata Singh Date: Tue, 8 Jul 2025 12:33:14 -0400 Subject: [PATCH 28/31] docs: cleanup (#5401) * docs: cleanup * fix nav * fix * fix nits --- docs/_scripts/extract_images.py | 70 -- docs/docs/additional-resources/index.md | 11 + docs/docs/agents/agents.md | 2 +- docs/docs/cloud/concepts/threads.md | 12 - docs/docs/examples/index.md | 23 + docs/docs/guides/index.md | 41 + .../tutorials/multi_agent/agent_supervisor.md | 2 +- docs/docs/tutorials/overview.md | 21 - docs/docs/tutorials/sql-agent.ipynb | 759 ------------------ docs/mkdocs.yml | 3 + 10 files changed, 80 insertions(+), 864 deletions(-) delete mode 100644 docs/_scripts/extract_images.py create mode 100644 docs/docs/additional-resources/index.md delete mode 100644 docs/docs/cloud/concepts/threads.md create mode 100644 docs/docs/examples/index.md create mode 100644 docs/docs/guides/index.md delete mode 100644 docs/docs/tutorials/overview.md delete mode 100644 docs/docs/tutorials/sql-agent.ipynb diff --git a/docs/_scripts/extract_images.py b/docs/_scripts/extract_images.py deleted file mode 100644 index 781ba0a22..000000000 --- a/docs/_scripts/extract_images.py +++ /dev/null @@ -1,70 +0,0 @@ -#!/usr/bin/env python3 -""" -Script to extract images from the graph-api.ipynb notebook and save them to assets folder. -""" - -import json -import base64 -import os -from pathlib import Path - -def extract_images_from_notebook(notebook_path, assets_dir): - """Extract images from notebook and save them to assets directory.""" - - # Read the notebook - with open(notebook_path, 'r') as f: - notebook = json.load(f) - - # Create assets directory if it doesn't exist - os.makedirs(assets_dir, exist_ok=True) - - image_count = 0 - - # Process each cell - for cell_idx, cell in enumerate(notebook['cells']): - if cell['cell_type'] == 'code': - # Check if this cell contains draw_mermaid_png - source = ''.join(cell.get('source', [])) - if 'draw_mermaid_png' in source: - print(f"Found draw_mermaid_png in cell {cell_idx}") - - # Check for outputs with images - if 'outputs' in cell: - for output_idx, output in enumerate(cell['outputs']): - if output.get('output_type') == 'display_data': - data = output.get('data', {}) - - # Check for PNG data - if 'image/png' in data: - png_data = data['image/png'] - - # Decode base64 data - try: - image_bytes = base64.b64decode(png_data) - - # Generate filename - image_count += 1 - filename = f"graph_api_image_{image_count}.png" - filepath = os.path.join(assets_dir, filename) - - # Save the image - with open(filepath, 'wb') as img_file: - img_file.write(image_bytes) - - print(f"Saved image: {filepath}") - - except Exception as e: - print(f"Error decoding image {image_count}: {e}") - - print(f"Extracted {image_count} images to {assets_dir}") - return image_count - -if __name__ == "__main__": - notebook_path = "docs/docs/how-tos/graph-api.ipynb" - assets_dir = "docs/docs/how-tos/assets" - - if os.path.exists(notebook_path): - count = extract_images_from_notebook(notebook_path, assets_dir) - print(f"Successfully extracted {count} images") - else: - print(f"Notebook not found: {notebook_path}") \ No newline at end of file diff --git a/docs/docs/additional-resources/index.md b/docs/docs/additional-resources/index.md new file mode 100644 index 000000000..6e4e7a93b --- /dev/null +++ b/docs/docs/additional-resources/index.md @@ -0,0 +1,11 @@ +# Additional resources + +This section contains additional resources for LangGraph. + +- [Community agents](../agents/prebuilt.md): A collection of prebuilt libraries that you can use in your LangGraph applications. +- [LangGraph Academy](https://academy.langchain.com/courses/intro-to-langgraph): A collection of courses that teach you how to use LangGraph. +- [Case studies](../adopters.md): A collection of case studies that show how LangGraph is used in production. +- [FAQ](../concepts/faq.md): A collection of frequently asked questions about LangGraph. +- [llms.txt](../llms-txt-overview.md): A list of documentation files in the `llms.txt` format that allow LLMs and agents to access our documentation. +- [LangChain Forum](https://forum.langchain.com/): A place to ask questions and get help from other LangGraph users. +- [Troubleshooting](../troubleshooting/errors/index.md.md): A collection of troubleshooting guides for common issues. \ No newline at end of file diff --git a/docs/docs/agents/agents.md b/docs/docs/agents/agents.md index b00184266..04ee419ce 100644 --- a/docs/docs/agents/agents.md +++ b/docs/docs/agents/agents.md @@ -52,7 +52,7 @@ agent.invoke( ) ``` -1. Define a tool for the agent to use. Tools can be defined as vanilla Python functions. For more advanced tool usage and customization, check the [tools](./tools.md) page. +1. Define a tool for the agent to use. Tools can be defined as vanilla Python functions. For more advanced tool usage and customization, check the [tools](../how-tos/tool-calling.md) page. 2. Provide a language model for the agent to use. To learn more about configuring language models for the agents, check the [models](./models.md) page. 3. Provide a list of tools for the model to use. 4. Provide a system prompt (instructions) to the language model used by the agent. diff --git a/docs/docs/cloud/concepts/threads.md b/docs/docs/cloud/concepts/threads.md deleted file mode 100644 index ffd48faa8..000000000 --- a/docs/docs/cloud/concepts/threads.md +++ /dev/null @@ -1,12 +0,0 @@ -# Threads - -A thread contains the accumulated state of a sequence of [runs](../../concepts/assistants.md#execution). When a run is executed, the [state](../../concepts/low_level.md#state) of the underlying graph of the assistant will be persisted to the thread. - -A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run. - -The state of a thread at a particular point in time is called a [checkpoint](../../concepts/persistence.md#checkpoints). Checkpoints are persisted and can be used to restore the state of a thread at a later time. - -## Learn more - -* For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](../../concepts/persistence.md). -* The LangGraph Platform API provides several endpoints for creating and managing threads and thread state. See the [API reference](../../cloud/reference/api/api_ref.html#tag/threads) for more details. diff --git a/docs/docs/examples/index.md b/docs/docs/examples/index.md new file mode 100644 index 000000000..84c721ac3 --- /dev/null +++ b/docs/docs/examples/index.md @@ -0,0 +1,23 @@ +# Examples + +The pages in this section provide end-to-end examples for the following topics: + +## General + +- [Template Applications](../concepts/template_applications.md): Create a LangGraph application from a template. +- [Agentic RAG](./rag/langgraph_agentic_rag.md): Build a retrieval agent that can decide when to use a retriever tool. +- [Agent Supervisor](./multi_agent/agent_supervisor.md): Build a supervisor agent that can manage a team of agents. +- [SQL agent](./sql/sql-agent.md): Build a SQL agent that can execute SQL queries and return the results. +- [Prebuilt chat UI](../agents/ui.md): Use a prebuilt chat UI to interact with any LangGraph agent. +- [Graph runs in LangSmith](../how-tos/run-id-langsmith.md): Use LangSmith to track and analyze graph runs. + +## LangGraph Platform + +- [Set up custom authentication](./auth/getting_started.md): Set up custom authentication for your LangGraph application. +- [Make conversations private](./auth/resource_auth.md): Make conversations private by using resource-based authentication. +- [Connect an authentication provider](./auth/add_auth_server.md): Connect an authentication provider to your LangGraph application. +- [Rebuild graph at runtime](../cloud/deployment/graph_rebuild.md): Rebuild a graph at runtime. +- [Use RemoteGraph](../how-tos/use-remote-graph.md): Use RemoteGraph to deploy your LangGraph application to a remote server. +- [Deploy CrewAI, AutoGen, and other frameworks](../how-tos/autogen-integration.md): Deploy CrewAI, AutoGen, and other frameworks with LangGraph. +- [Integrate LangGraph into a React app](../cloud/how-tos/use_stream_react.md) +- [Implement Generative User Interfaces with LangGraph](../cloud/how-tos/generative_ui_react.md) \ No newline at end of file diff --git a/docs/docs/guides/index.md b/docs/docs/guides/index.md new file mode 100644 index 000000000..1d57f63ea --- /dev/null +++ b/docs/docs/guides/index.md @@ -0,0 +1,41 @@ +# Guides + +The pages in this section provide a conceptual overview and how-tos for the following topics: + +## LangGraph APIs + +- [Graph API](../concepts/low_level.md): Use the Graph API to define workflows using a graph paradigm. +- [Functional API](../concepts/functional_api.md): Use Functional API to build workflows using a functional paradigm without thinking about the graph structure. +- [Runtime](../concepts/pregel.md): Pregel implements LangGraph's runtime, managing the execution of LangGraph applications. + +## Core capabilities + +These capabilities are available in both LangGraph OSS and the LangGraph Platform. + +- [Streaming](../concepts/streaming.md): Stream outputs from a LangGraph graph. +- [Persistence](../concepts/persistence.md): Persist the state of a LangGraph graph. +- [Durable execution](../concepts/durable_execution.md): Save progress at key points in the graph execution. +- [Memory](../concepts/memory.md): Remember information about previous interactions. +- [Context](../agents/context.md): Pass outside data to a LangGraph graph to provide context for the graph execution. +- [Models](../agents/models.md): Integrate various LLMs into your LangGraph application. +- [Tools](../concepts/tools.md): Interface directly with external systems. +- [Human-in-the-loop](../concepts/human_in_the_loop.md): Enable human intervention at any point in a workflow. +- [Breakpoints](../concepts/breakpoints.md): Pause the execution of a LangGraph graph at a specific point. +- [Time travel](../concepts/time-travel.md): Travel back in time to a specific point in the execution of a LangGraph graph. +- [Subgraphs](../concepts/subgraphs.md): Build modular graphs. +- [Multi-agent](../concepts/multi_agent.md): Break down a complex workflow into multiple agents. +- [MCP](../concepts/mcp.md): Use MCP servers in a LangGraph graph. +- [Evaluation](../agents/evals.md): Use LangSmith to evaluate your graph's performance. + +## Platform-only capabilities + +These capabilities are only available in [LangGraph Platform](../concepts/langgraph_platform.md). + +- [Authentication and access control](../concepts/auth.md): Authenticate and authorize users to access a Langraph graph. +- [Assistants](../concepts/assistants.md): Build assistants that can be used to interact with a LangGraph graph. +- [Double-texting](../concepts/double_texting.md): Handle double-texting (consecutive messages before a first response is returned) in a LangGraph graph. +- [Webhooks](../cloud/concepts/webhooks.md): Send webhooks to a LangGraph graph. +- [Cron jobs](../cloud/concepts/cron_jobs.md): Schedule jobs to run at a specific time. +- [Server customization](../how-tos/http/custom_lifespan.md): Customize the server that runs a LangGraph graph. +- [Data management](../cloud/concepts/data_storage_and_privacy.md): Manage data in a LangGraph graph. +- [Deployment](../concepts/deployment_options.md): Deploy a LangGraph graph to a server. \ No newline at end of file diff --git a/docs/docs/tutorials/multi_agent/agent_supervisor.md b/docs/docs/tutorials/multi_agent/agent_supervisor.md index ba8f84ff6..71335c8db 100644 --- a/docs/docs/tutorials/multi_agent/agent_supervisor.md +++ b/docs/docs/tutorials/multi_agent/agent_supervisor.md @@ -206,7 +206,7 @@ Name: tavily_search ### Math agent -For math agent tools we will use [vanilla Python functions](../../agents/tools.md#define-simple-tools): +For math agent tools we will use [vanilla Python functions](../../how-tos/tool-calling.md#define-a-tool): ```python def add(a: float, b: float): diff --git a/docs/docs/tutorials/overview.md b/docs/docs/tutorials/overview.md deleted file mode 100644 index 6eacbd4e4..000000000 --- a/docs/docs/tutorials/overview.md +++ /dev/null @@ -1,21 +0,0 @@ -# Examples - -The pages in this section provide end-to-end examples for the following topics: - -## General - -- [Agentic RAG](./rag/langgraph_adaptive_rag.ipynb) -- [Agent Supervisor](./multi_agent/agent_supervisor.md) -- [SQL agent](./sql/sql-agent.md) -- [Graph runs in LangSmith](../how-tos/run-id-langsmith.md) - -## LangGraph Platform - -- [Set up custom authentication](./auth/getting_started.md) -- [Make conversations private](./auth/resource_auth.md) -- [Connect an authentication provider](./auth/add_auth_server.md) -- [Rebuild graph at runtime](../cloud/deployment/graph_rebuild.md) -- [Use RemoteGraph](../how-tos/use-remote-graph.md) -- [Deploy CrewAI, AutoGen, and other frameworks](../how-tos/autogen-integration.md) -- [Integrate LangGraph into a React app](../cloud/how-tos/use_stream_react.md) -- [Implement Generative User Interfaces with LangGraph](../cloud/how-tos/generative_ui_react.md) \ No newline at end of file diff --git a/docs/docs/tutorials/sql-agent.ipynb b/docs/docs/tutorials/sql-agent.ipynb deleted file mode 100644 index a06626f40..000000000 --- a/docs/docs/tutorials/sql-agent.ipynb +++ /dev/null @@ -1,759 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cb71c4a7-9f99-4f08-be34-b3ba81ac075f", - "metadata": {}, - "source": [ - "# Build a SQL agent\n", - "\n", - "In this tutorial, we will walk through how to build an agent that can answer questions about a SQL database.\n", - "\n", - "At a high level, the agent will:\n", - "\n", - "1. Fetch the available tables from the database\n", - "2. Decide which tables are relevant to the question\n", - "3. Fetch the schemas for the relevant tables\n", - "4. Generate a query based on the question and information from the schemas\n", - "5. Double-check the query for common mistakes using an LLM\n", - "6. Execute the query and return the results\n", - "7. Correct mistakes surfaced by the database engine until the query is successful\n", - "8. Formulate a response based on the results\n", - "\n", - "!!! warning \"Security note\"\n", - "\n", - " Building Q&A systems of SQL databases requires executing model-generated SQL queries. There are inherent risks in doing this. Make sure that your database connection permissions are always scoped as narrowly as possible for your agent's needs. This will mitigate though not eliminate the risks of building a model-driven system.\n", - "\n", - "## 1. Setup\n", - "\n", - "Let's first install some dependencies. This tutorial uses SQL database and tool abstractions from [langchain-community](https://python.langchain.com/docs/concepts/architecture/#langchain-community). We will also require a LangChain [chat model](https://python.langchain.com/docs/concepts/chat_models/)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1b613286-41c3-4ca5-9514-36aac0121a5a", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture --no-stderr\n", - "%pip install -U langgraph langchain_community \"langchain[openai]\"" - ] - }, - { - "cell_type": "markdown", - "id": "f5258ad0-39e1-418e-bddb-56e9aab1b43a", - "metadata": {}, - "source": [ - "

\n", - "

Set up LangSmith for LangGraph development

\n", - "

\n", - " Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started here. \n", - "

\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "62886241-caa7-40bd-8867-096a97acaf40", - "metadata": {}, - "source": [ - "### Select a LLM\n", - "\n", - "First we [initialize our LLM](https://python.langchain.com/docs/how_to/chat_models_universal_init/). Any model supporting [tool-calling](https://python.langchain.com/docs/integrations/chat/#featured-providers) should work. We use OpenAI below." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "2b981efc-bcc9-49f4-aa66-4c15a76210a4", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.chat_models import init_chat_model\n", - "\n", - "llm = init_chat_model(\"openai:gpt-4.1\")" - ] - }, - { - "cell_type": "markdown", - "id": "804ca604-7ac4-4229-a3e9-3a9825eeefa4", - "metadata": {}, - "source": [ - "### Configure the database\n", - "\n", - "We will be creating a SQLite database for this tutorial. SQLite is a lightweight database that is easy to set up and use. We will be loading the `chinook` database, which is a sample database that represents a digital media store.\n", - "Find more information about the database [here](https://www.sqlitetutorial.net/sqlite-sample-database/).\n", - "\n", - "For convenience, we have hosted the database (`Chinook.db`) on a public GCS bucket." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b58ba6b7-5f80-4970-bc29-961de0e24fbd", - "metadata": {}, - "outputs": [], - "source": [ - "import requests\n", - "\n", - "url = \"https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db\"\n", - "\n", - "response = requests.get(url)\n", - "\n", - "if response.status_code == 200:\n", - " # Open a local file in binary write mode\n", - " with open(\"Chinook.db\", \"wb\") as file:\n", - " # Write the content of the response (the file) to the local file\n", - " file.write(response.content)\n", - " print(\"File downloaded and saved as Chinook.db\")\n", - "else:\n", - " print(f\"Failed to download the file. Status code: {response.status_code}\")" - ] - }, - { - "cell_type": "markdown", - "id": "5ae005fb-e7b0-43f1-95b2-565d19c82610", - "metadata": {}, - "source": [ - "We will use a handy SQL database wrapper available in the `langchain_community` package to interact with the database. The wrapper provides a simple interface to execute SQL queries and fetch results:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "18822c9b-6df2-4a5c-a12d-046d2b2131c9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dialect: sqlite\n", - "Available tables: ['Album', 'Artist', 'Customer', 'Employee', 'Genre', 'Invoice', 'InvoiceLine', 'MediaType', 'Playlist', 'PlaylistTrack', 'Track']\n", - "Sample output: [(1, 'AC/DC'), (2, 'Accept'), (3, 'Aerosmith'), (4, 'Alanis Morissette'), (5, 'Alice In Chains')]\n" - ] - } - ], - "source": [ - "from langchain_community.utilities import SQLDatabase\n", - "\n", - "db = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")\n", - "\n", - "print(f\"Dialect: {db.dialect}\")\n", - "print(f\"Available tables: {db.get_usable_table_names()}\")\n", - "print(f'Sample output: {db.run(\"SELECT * FROM Artist LIMIT 5;\")}')" - ] - }, - { - "cell_type": "markdown", - "id": "3bf47b6c-30dd-405f-8a03-21327af3c764", - "metadata": {}, - "source": [ - "### Tools for database interactions\n", - "\n", - "`langchain-community` implements some built-in tools for interacting with our `SQLDatabase`, including tools for listing tables, reading table schemas, and checking and running queries:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "aab9e00a-aa28-494a-a95a-a6638ea03bd1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sql_db_query: Input to this tool is a detailed and correct SQL query, output is a result from the database. If the query is not correct, an error message will be returned. If an error is returned, rewrite the query, check the query, and try again. If you encounter an issue with Unknown column 'xxxx' in 'field list', use sql_db_schema to query the correct table fields.\n", - "\n", - "sql_db_schema: Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. Be sure that the tables actually exist by calling sql_db_list_tables first! Example Input: table1, table2, table3\n", - "\n", - "sql_db_list_tables: Input is an empty string, output is a comma-separated list of tables in the database.\n", - "\n", - "sql_db_query_checker: Use this tool to double check if your query is correct before executing it. Always use this tool before executing a query with sql_db_query!\n", - "\n" - ] - } - ], - "source": [ - "from langchain_community.agent_toolkits import SQLDatabaseToolkit\n", - "\n", - "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n", - "\n", - "tools = toolkit.get_tools()\n", - "\n", - "for tool in tools:\n", - " print(f\"{tool.name}: {tool.description}\\n\")" - ] - }, - { - "cell_type": "markdown", - "id": "6baf93d3-afd5-484e-9bf7-3ff20401e06f", - "metadata": {}, - "source": [ - "## 2. Using a prebuilt agent\n", - "\n", - "Given these tools, we can initialize a pre-built agent in a single line. To customize our agents behavior, we write a descriptive system prompt." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f6b9f10f-c6cb-43bc-b671-79aadc71280b", - "metadata": {}, - "outputs": [], - "source": [ - "from langgraph.prebuilt import create_react_agent\n", - "\n", - "system_prompt = \"\"\"\n", - "You are an agent designed to interact with a SQL database.\n", - "Given an input question, create a syntactically correct {dialect} query to run,\n", - "then look at the results of the query and return the answer. Unless the user\n", - "specifies a specific number of examples they wish to obtain, always limit your\n", - "query to at most {top_k} results.\n", - "\n", - "You can order the results by a relevant column to return the most interesting\n", - "examples in the database. Never query for all the columns from a specific table,\n", - "only ask for the relevant columns given the question.\n", - "\n", - "You MUST double check your query before executing it. If you get an error while\n", - "executing a query, rewrite the query and try again.\n", - "\n", - "DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the\n", - "database.\n", - "\n", - "To start you should ALWAYS look at the tables in the database to see what you\n", - "can query. Do NOT skip this step.\n", - "\n", - "Then you should query the schema of the most relevant tables.\n", - "\"\"\".format(\n", - " dialect=db.dialect,\n", - " top_k=5,\n", - ")\n", - "\n", - "agent = create_react_agent(\n", - " llm,\n", - " tools,\n", - " prompt=system_prompt,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "78b1c7cf-9a0f-48c3-baec-be8101bc3c77", - "metadata": {}, - "source": [ - "!!! note\n", - "\n", - " This system prompt includes a number of instructions, such as always running specific tools before or after others. In the [next section](#customizing-the-agent), we will enforce these behaviors through the graph's structure, providing us a greater degree of control and allowing us to simplify the prompt.\n", - "\n", - "\n", - "Let's run this agent on a sample query and observe its behavior:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9317c234-6251-4723-a335-7fee6d11ac2b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "Which genre on average has the longest tracks?\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " sql_db_list_tables (call_d8lCgywSroCgpVl558nmXKwA)\n", - " Call ID: call_d8lCgywSroCgpVl558nmXKwA\n", - " Args:\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: sql_db_list_tables\n", - "\n", - "Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " sql_db_schema (call_nNf6IIUcwMYLIkE0l6uWkZHe)\n", - " Call ID: call_nNf6IIUcwMYLIkE0l6uWkZHe\n", - " Args:\n", - " table_names: Genre, Track\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: sql_db_schema\n", - "\n", - "\n", - "CREATE TABLE \"Genre\" (\n", - "\t\"GenreId\" INTEGER NOT NULL, \n", - "\t\"Name\" NVARCHAR(120), \n", - "\tPRIMARY KEY (\"GenreId\")\n", - ")\n", - "\n", - "/*\n", - "3 rows from Genre table:\n", - "GenreId\tName\n", - "1\tRock\n", - "2\tJazz\n", - "3\tMetal\n", - "*/\n", - "\n", - "\n", - "CREATE TABLE \"Track\" (\n", - "\t\"TrackId\" INTEGER NOT NULL, \n", - "\t\"Name\" NVARCHAR(200) NOT NULL, \n", - "\t\"AlbumId\" INTEGER, \n", - "\t\"MediaTypeId\" INTEGER NOT NULL, \n", - "\t\"GenreId\" INTEGER, \n", - "\t\"Composer\" NVARCHAR(220), \n", - "\t\"Milliseconds\" INTEGER NOT NULL, \n", - "\t\"Bytes\" INTEGER, \n", - "\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n", - "\tPRIMARY KEY (\"TrackId\"), \n", - "\tFOREIGN KEY(\"MediaTypeId\") REFERENCES \"MediaType\" (\"MediaTypeId\"), \n", - "\tFOREIGN KEY(\"GenreId\") REFERENCES \"Genre\" (\"GenreId\"), \n", - "\tFOREIGN KEY(\"AlbumId\") REFERENCES \"Album\" (\"AlbumId\")\n", - ")\n", - "\n", - "/*\n", - "3 rows from Track table:\n", - "TrackId\tName\tAlbumId\tMediaTypeId\tGenreId\tComposer\tMilliseconds\tBytes\tUnitPrice\n", - "1\tFor Those About To Rock (We Salute You)\t1\t1\t1\tAngus Young, Malcolm Young, Brian Johnson\t343719\t11170334\t0.99\n", - "2\tBalls to the Wall\t2\t2\t1\tNone\t342562\t5510424\t0.99\n", - "3\tFast As a Shark\t3\t2\t1\tF. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman\t230619\t3990994\t0.99\n", - "*/\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " sql_db_query_checker (call_urTRmtiGtTxkwHtscec7Fd2K)\n", - " Call ID: call_urTRmtiGtTxkwHtscec7Fd2K\n", - " Args:\n", - " query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgMilliseconds\n", - "FROM Track\n", - "JOIN Genre ON Track.GenreId = Genre.GenreId\n", - "GROUP BY Genre.Name\n", - "ORDER BY AvgMilliseconds DESC\n", - "LIMIT 1;\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: sql_db_query_checker\n", - "\n", - "```sql\n", - "SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgMilliseconds\n", - "FROM Track\n", - "JOIN Genre ON Track.GenreId = Genre.GenreId\n", - "GROUP BY Genre.Name\n", - "ORDER BY AvgMilliseconds DESC\n", - "LIMIT 1;\n", - "```\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " sql_db_query (call_RNMqyUEMv0rvy0UxSwrXY2AV)\n", - " Call ID: call_RNMqyUEMv0rvy0UxSwrXY2AV\n", - " Args:\n", - " query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgMilliseconds\n", - "FROM Track\n", - "JOIN Genre ON Track.GenreId = Genre.GenreId\n", - "GROUP BY Genre.Name\n", - "ORDER BY AvgMilliseconds DESC\n", - "LIMIT 1;\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: sql_db_query\n", - "\n", - "[('Sci Fi & Fantasy', 2911783.0384615385)]\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "The genre with the longest average track length is \"Sci Fi & Fantasy,\" with an average duration of about 2,911,783 milliseconds (approximately 48.5 minutes) per track.\n" - ] - } - ], - "source": [ - "question = \"Which genre on average has the longest tracks?\"\n", - "\n", - "for step in agent.stream(\n", - " {\"messages\": [{\"role\": \"user\", \"content\": question}]},\n", - " stream_mode=\"values\",\n", - "):\n", - " step[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "6b2afc54-0a26-40f8-bca2-01cbb2fc3443", - "metadata": {}, - "source": [ - "This worked well enough: the agent correctly listed the tables, obtained the schemas, wrote a query, checked the query, and ran it to inform its final response.\n", - "\n", - "!!! tip\n", - "\n", - " You can inspect all aspects of the above run, including steps taken, tools invoked, what prompts were seen by the LLM, and more in the [LangSmith trace](https://smith.langchain.com/public/bd594960-73e3-474b-b6f2-db039d7c713a/r)." - ] - }, - { - "cell_type": "markdown", - "id": "2ed79712-ca6b-47dc-9bc9-7cb6fc50a8aa", - "metadata": {}, - "source": [ - "## 3. Customizing the agent\n", - "\n", - "The prebuilt agent lets us get started quickly, but at each step the agent has access to the full set of tools. Above, we relied on the system prompt to constrain its behavior— for example, we instructed the agent to always start with the \"list tables\" tool, and to always run a query-checker tool before executing the query.\n", - "\n", - "We can enforce a higher degree of control in LangGraph by customizing the agent. Below, we implement a simple ReAct-agent setup, with dedicated nodes for specific tool-calls. We will use the same [state](../../concepts/low_level/#state) as the pre-built agent.\n", - "\n", - "We construct dedicated nodes for the following steps:\n", - "\n", - "- Listing DB tables\n", - "- Calling the \"get schema\" tool\n", - "- Generating a query\n", - "- Checking the query\n", - "\n", - "Putting these steps in dedicated nodes lets us (1) force tool-calls when needed, and (2) customize the prompts associated with each step." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "992f6ce6-f932-4f44-994c-c0ba5032dbd1", - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Literal\n", - "from langchain_core.messages import AIMessage\n", - "from langchain_core.runnables import RunnableConfig\n", - "from langgraph.graph import END, START, MessagesState, StateGraph\n", - "from langgraph.prebuilt import ToolNode\n", - "\n", - "\n", - "get_schema_tool = next(tool for tool in tools if tool.name == \"sql_db_schema\")\n", - "get_schema_node = ToolNode([get_schema_tool], name=\"get_schema\")\n", - "\n", - "run_query_tool = next(tool for tool in tools if tool.name == \"sql_db_query\")\n", - "run_query_node = ToolNode([run_query_tool], name=\"run_query\")\n", - "\n", - "\n", - "# Example: create a predetermined tool call\n", - "def list_tables(state: MessagesState):\n", - " tool_call = {\n", - " \"name\": \"sql_db_list_tables\",\n", - " \"args\": {},\n", - " \"id\": \"abc123\",\n", - " \"type\": \"tool_call\",\n", - " }\n", - " tool_call_message = AIMessage(content=\"\", tool_calls=[tool_call])\n", - "\n", - " list_tables_tool = next(tool for tool in tools if tool.name == \"sql_db_list_tables\")\n", - " tool_message = list_tables_tool.invoke(tool_call)\n", - " response = AIMessage(f\"Available tables: {tool_message.content}\")\n", - "\n", - " return {\"messages\": [tool_call_message, tool_message, response]}\n", - "\n", - "\n", - "# Example: force a model to create a tool call\n", - "def call_get_schema(state: MessagesState):\n", - " # Note that LangChain enforces that all models accept `tool_choice=\"any\"`\n", - " # as well as `tool_choice=`.\n", - " llm_with_tools = llm.bind_tools([get_schema_tool], tool_choice=\"any\")\n", - " response = llm_with_tools.invoke(state[\"messages\"])\n", - "\n", - " return {\"messages\": [response]}\n", - "\n", - "\n", - "generate_query_system_prompt = \"\"\"\n", - "You are an agent designed to interact with a SQL database.\n", - "Given an input question, create a syntactically correct {dialect} query to run,\n", - "then look at the results of the query and return the answer. Unless the user\n", - "specifies a specific number of examples they wish to obtain, always limit your\n", - "query to at most {top_k} results.\n", - "\n", - "You can order the results by a relevant column to return the most interesting\n", - "examples in the database. Never query for all the columns from a specific table,\n", - "only ask for the relevant columns given the question.\n", - "\n", - "DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n", - "\"\"\".format(\n", - " dialect=db.dialect,\n", - " top_k=5,\n", - ")\n", - "\n", - "\n", - "def generate_query(state: MessagesState):\n", - " system_message = {\n", - " \"role\": \"system\",\n", - " \"content\": generate_query_system_prompt,\n", - " }\n", - " # We do not force a tool call here, to allow the model to\n", - " # respond naturally when it obtains the solution.\n", - " llm_with_tools = llm.bind_tools([run_query_tool])\n", - " response = llm_with_tools.invoke([system_message] + state[\"messages\"])\n", - "\n", - " return {\"messages\": [response]}\n", - "\n", - "\n", - "check_query_system_prompt = \"\"\"\n", - "You are a SQL expert with a strong attention to detail.\n", - "Double check the {dialect} query for common mistakes, including:\n", - "- Using NOT IN with NULL values\n", - "- Using UNION when UNION ALL should have been used\n", - "- Using BETWEEN for exclusive ranges\n", - "- Data type mismatch in predicates\n", - "- Properly quoting identifiers\n", - "- Using the correct number of arguments for functions\n", - "- Casting to the correct data type\n", - "- Using the proper columns for joins\n", - "\n", - "If there are any of the above mistakes, rewrite the query. If there are no mistakes,\n", - "just reproduce the original query.\n", - "\n", - "You will call the appropriate tool to execute the query after running this check.\n", - "\"\"\".format(dialect=db.dialect)\n", - "\n", - "\n", - "def check_query(state: MessagesState):\n", - " system_message = {\n", - " \"role\": \"system\",\n", - " \"content\": check_query_system_prompt,\n", - " }\n", - "\n", - " # Generate an artificial user message to check\n", - " tool_call = state[\"messages\"][-1].tool_calls[0]\n", - " user_message = {\"role\": \"user\", \"content\": tool_call[\"args\"][\"query\"]}\n", - " llm_with_tools = llm.bind_tools([run_query_tool], tool_choice=\"any\")\n", - " response = llm_with_tools.invoke([system_message, user_message])\n", - " response.id = state[\"messages\"][-1].id\n", - "\n", - " return {\"messages\": [response]}" - ] - }, - { - "cell_type": "markdown", - "id": "cd23a432-09c1-428c-88a7-6b4d1fe55577", - "metadata": {}, - "source": [ - "Finally, we assemble these steps into a workflow using the Graph API. We define a [conditional edge](../../concepts/low_level/#conditional-edges) at the query generation step that will route to the query checker if a query is generated, or end if there are no tool calls present, such that the LLM has delivered a response to the query." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8b3ce09d-0eec-456d-8447-d04f79ccfc23", - "metadata": {}, - "outputs": [], - "source": [ - "def should_continue(state: MessagesState) -> Literal[END, \"check_query\"]:\n", - " messages = state[\"messages\"]\n", - " last_message = messages[-1]\n", - " if not last_message.tool_calls:\n", - " return END\n", - " else:\n", - " return \"check_query\"\n", - "\n", - "\n", - "builder = StateGraph(MessagesState)\n", - "builder.add_node(list_tables)\n", - "builder.add_node(call_get_schema)\n", - "builder.add_node(get_schema_node, \"get_schema\")\n", - "builder.add_node(generate_query)\n", - "builder.add_node(check_query)\n", - "builder.add_node(run_query_node, \"run_query\")\n", - "\n", - "builder.add_edge(START, \"list_tables\")\n", - "builder.add_edge(\"list_tables\", \"call_get_schema\")\n", - "builder.add_edge(\"call_get_schema\", \"get_schema\")\n", - "builder.add_edge(\"get_schema\", \"generate_query\")\n", - "builder.add_conditional_edges(\n", - " \"generate_query\",\n", - " should_continue,\n", - ")\n", - "builder.add_edge(\"check_query\", \"run_query\")\n", - "builder.add_edge(\"run_query\", \"generate_query\")\n", - "\n", - "agent = builder.compile()" - ] - }, - { - "cell_type": "markdown", - "id": "c377e9dd-92a7-45d4-91bf-408a6f2af8ca", - "metadata": {}, - "source": [ - "We visualize the application below:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f728ed5f-7bcd-489e-9174-4e4725962335", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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a1NHDc9nSX0JCTwSfPlZaWtK//1CE0LSpcw8e2r18xY96evq8gAk/9B30VWvOzm4HD5w6FXz0j4O7hcIKRweX9et2slisKus0N7dU/ifUQHCvlFbt0d8FcjnVubsW2YU0D0lPiytKK72H6TXNy0G3FgBMQbcWNIUVKxckVPOVkX79hsyetaDJK2oGIJygKSxZtEpcKa5yEYfNafJymgcIJ2gKOjq6ZJfQ/MCYEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQThbNRUOjfr1tJKgWlQqlaPWdBfVQThbNS0DZnZaRR1WBAghlJtewdWFcIImYd6WIxRIZVL4Tm+dlBSILR3U6rBi44BwtmpUGvIepnf7dCbZhTQDt09ndemvw1ShNNkrwkwIAOWmiy4e+OjcXVtLn8niwBj0C+IKWWGW8OWT4j4TDE3bsJvypSGcACGExELZ8zvF+R9FZcUNnKm9ceXm5enp6TXdTqp66lp0LQOmaw9NjnpT/9mCcAIcde3aNSIiAtsbJTQNGHMCgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJ8BR27ZtKRQc5pQmE4QT4Oj169cw3TmEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMUeBbcwAfvXv3ptPpVCo1KytLT0+PSqUihIyNjQ8fPkx2aSSgk10AAP/Jy8uj0WgIIQqFkp+fjxDicDg8Ho/susgB3VqAEU9Pz6+6cjY2Nn5+fuRVRCYIJ8DIpEmTNDU1FQ9VVVXHjBlDakVkgnACjHTu3NnOzk7x0MbGxt/fn9SKyAThBHiZMGECl8sldputdrRJgHACvHTp0sXW1hYhZGFh0bt3b7LLIRMcrW32ZFJ5flalTCoju5BGM3zA1LwPlSMGTsxOE5JdS6OhUinahkw6ox6T8cJ5zmZMUCx5cLkgOb7Uylm9JL+S7HJATbi6jJQEgUU7Va8BOpp6jLpsAuFsrspLpMHb03uPM9EyYJJdC6irkvzKf059HDbXVEOn9k4rhLNZkkrkf6xIHr/KhuxCQEOEbEset9yCrUareTUIZ7N0/2K+jhHbzF6V7EJAQ2SlVHx4U9prlH7Nq8HR2mYp43W5unadxi0AQ1wdRtrL8lpXg3A2SzQmVUMHhprNFUeDrsqlS8S1rAbhbJYKs0RyGYxHmrGCLBGFUstvEMIJAKYgnABgCsIJAKYgnABgCsIJAKYgnABgCsIJAKYgnABgCsIJAKYgnABgCsIJAKYgnKBq586H+PXuTPw8ZJjfib+a05zrV/++4OPrIZFIyC7ku0A4AZlSUt7zxgwguwpMQTgBmV6/eUl2CfiC2fdai/T01B2/boiLe2FsZNK9e68pk2czmUyE0PkLoY8f309KSmCyWK4u7adOnWtibNqwl7h85VxY2F8lpSWent2mTp7DGzNg1coNvr36IISu37hy+cq5lJR3VlZtevn0Hj5sNIVCOXrsANFb9vH1mDN74cgRY6trWS6Xnzt/+saNqxkf0izMrTw8PKdMnk3cVaW694UQKijIX7fh58TEOFNTc17AhP79hhDPJybGHT9x8NWrRK6mVhfP7hMnzFBVVUUIrQ1aTqFQunh237ZjHY1Gs2/r+MuaLRcvnTl+4qCGBrdP7wGzZs6nUCgIoUeP7kf8eyMu/kVJCb+dvdP48dPc3Twa9qHVAPacrUJ2dta8/012dnLbsX1/QMCE2xHXd+/ZihCKj4/Zs3ebo6NrUND25cvWFhUVbti4qmEvkfQq8dddm7y9/f46fr5nD7+g9SsQQsRtwm7dvr5l61o7W/vgk5enTZ179lzw3n07EEKTJ83iBUwwMDD893ZUDclECJ0/H3Ly1J8jho8JCb46cODwv69dDAk9UcP7QgjR6fTde7eOHzdt544D9vaOu37bnJOTjRD68DFjydI5QpFw756j69ZuT05+u3DRDGJ0SqfTExJjExJjz4SGH9j3V0Ji7PyF02Uy6dXLd9cEbg47c/LJk0iEkFAo3LBplUgkWr5s7cYNu8zNLVeuWlhYWNCwz60GsOdsFc6eC2apqEyeNItGo7V378hkMl+/fokQcnBwPnokzNTUnE6nI4QklZU/r1rIL+FzNbj1fYmbN69qa+tMnjSLTqd7efV48zbp5ct4YtG1axddXNwXzF+OENLS0p48cdbW7UHjxkzR0tKuY+Oxcc/btnXo02cAQmhA/6Hu7h0rystreF8IIYlEMmjgiM6dvBBC+vqGt26FJ71KMDAwvHUrnEFnrFu7ncvVRAgtWbx69NiBDyLv9PT2QwiJxeJ5c5cwGAwuV9Paqo1EKpk8aRZCyN3NQ1NT633yW0/PbioqKocPhrDZbKKFdvZOly6fjU+I8e7hW98PrWYQzlYhOfmtra090Q9ECPXtM7Bvn4EIIRqNlpn54fd9O5JeJZSVlRFLi4sKGxDO5JR37do5ESFHCPXo7nv8xCGEkEwmS0iMnTB+umJNd/eOMpksLv5F3f83Ozm5Hjy0Z+u2IBcX9y5deig63tW9L4KrS3viB02uFkJIJBQihBITY+3tHYlcIYQMDY2MjU3j4l8Q4TQxMWMwPk3OxOZwdLR1Fa2pclQFglLi5/LyssNH9sbERhcU5H/60IqL6vuJ1QrC2SqUlQk0NbW+fT4y8u6qwMVjx0yeOWO+jY1tVPSTpcvmNewlBIJSfX1DxUPF/36xWFxZWXnkz31H/tz3+fpFRYV1b3zE8DEcjmrkw7tbtq6l0+k9e/rPnP6jrq5ede+LoPhLQQwUFXW+ev3Sx/eLIWLR/3dKiX64wlcPCTk52fMXTmvv3mn1yo0ODs4UCsW/j2fd30vdQThbBVVVtbLysm+fv3rtgrOz27Spc4mHij1DA7BYKpLK/2adLyj8tEtRUVHhcDi9/fv3+HI/aWxUj8NOVCp1QP+hA/oPTU1Nfv786bETB8vKBBvX/1rd+6qBto6us7Mb0VlV4GpoVr/F1+7c/UcsFi9ftpbNZitpn0mAcLYKbds6XLl6TiKREDuT2xE3wsMvbdm8p6SEb2hgpFjt/v2IBr+EiYnZ27evFA8jI+8ofraxsSsVlCqOZ1ZWVmZlfdTXN6h74zduXLWza2dlZWNpaW1paV0qKP372oUa3lcNTdlY2978529Xl/aKvWJqarKpqXndiykp4auraxDJRAjdvXe77tvWCxytbRX69xsiFot3/roxKvrJ/Qf/Hjq8R0dXj0ajtbGxexb1+EVMlEQiOXP2FLFydk5WA16iq5d3WlpK8Oljcrn8WdTj+PgYxaLpU+dFRt65Fn5JJpPFx8cErVuxaMkssViMEDI1NS8oyH/w4E5GRloNjd+OuB74y08PH97jl/AfP35w/0GEk6NrDe+rhqZGjBgrk8n27tshFAozMtL+OLh7yrSA5JR3dX+n1ta2BQX5l6+ck0gkT54+fP78KZermZubXfcW6gj2nK2Cqan55k27t29fF379MovF6tN7wLRp8xBCU6bMKS8vW7V6UUVFxbChvOXL1mZlfVy+4seVP6+v70v06N5r6JBRx08cDDtz0sHBedq0eXPnTSIOrjg7ux08cOpU8NE/Du4WCiscHVzWr9vJYrEQQp6duzk7ua1es2TihBmTJs6orvHFi1bt/X37ytWLEELa2joD+g8dOWJcDe+rBhrqGkcOh4aEHJ85e1x6eqq9veNPS1bb2drX/Z369uqTlpZ84q9Dv+7a1NHDc9nSX0JCTwSfPubq2qFTxy71+cxqAbdjaJb2L30/eqk1rT73k1M2iUSSmprcps2n+1InvUqcM3fioT+CFc+Az53c8H7Ghlp+g9CtBY0jPiFm+swxv+3ekp2d9fJl/G+/bXZ0dLGxsSW7rmYMurWgrlasXJDw2Ujyc/36DZk9a8HiRSvDr1+eMm2Umpq6RwfPWbMWfH4Oo2bBp4+dPn2sykUWltZ7d//5HYU3VxBOUFdLFq0SV1Z9fw8Om0NcuzOg/9CGNT5w4HAfn6pvMk+ntdL/pa30bYMG0NHRrcNaDaSupq6upq689psjGHMCgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIZ7NkaKGCEEZfSQH1ZWCuQqHW8huEcDZLUom8IFtIdhWggYrzxBUCKbWm74QjCGdzZeWkWpgtIrsK0EAFWSIbZ7VaV4NwNksdfLXevSjJeF1OdiGg3nLThbF3Cz371z5nL8yE0IwFb023bc/V1GVqG7HIrgXUgkJBhdmikoLKhIdF43+2qMsXXSGczduLf4tTXpbRaJSc9BY1BJVIpHR6bWOyZkXfTEUqkVm2U+3gV+1Eu1+BcAIcde3aNSIigpgErNWCMScAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAoApCCcAmIJwAhy5uLiQXQL5IJwAR3FxcWSXQD4IJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYosjlcrJrAOCTESNGMJlMKpWalJRkbW3NYDCoVCqXy/3999/JLo0EdLILAOA/qampxA8UCiUlJQUhxGQyFyxYQHZd5IBuLcCIq6urTCb7/Blzc/NRo0aRVxGZIJwAI2PHjtXW1lY8ZLFYAQEBpFZEJggnwEivXr3Mzc0VD01NTYcOHUpqRWSCcAK8BAQEqKmpEbtNHo9HdjlkgnACvPTp08fS0lIul5uYmLTm3SYcrW29ykulZJdQrZHDxmVm/MYbORHnIlVUaVQl79rgPGfr8vaFIOZucX6miKkCnabvQ0FsVZp7T812nTSU9QoQztYj9i4/412FS3dtLQMm2bW0BEU54pePi7X06Z37atdh9XqDcLYWUf8UFeRUeg3UJ7uQlibqZj6DhboN0m30lqFv0yrw8yQ56SJIpjJ49NYV8KW5GaJGbxnC2SrkZFRQqBSyq2ix6HRqTrqw0ZuFcLYKgmKJvhmb7CpaLF1TFQFf0ujNwqmUVkFUIYNjC8ojEctEZbI6rFg/sOcEAFMQTgAwBeEEAFMQTgAwBeEEAFMQTgAwBeEEAFMQTgAwBeEEAFMQTgAwBeEEAFMQTtAMjAz44fCRVjfpO4QTNKa1QcuvhV8iu4oWAsIJGtPr1y/JLqHlgK+MgaoVFRVu2hyY+DLO3Mxy8OCRHz6k33/w7/GjZxFChYUF+/bvTEiMFQqFHTt2mTBumpmZBULIx9cDIbRt+7r9B369culODY2np6cePXYgJjZaLpc7OrrwRk1wdnZDCEml0jNnTx0/cRAh5NDOedLEmcTzCCE6nXH+QuiBP3YxmUwnJ7cVy4O4GlyEkEQiOfLnvsdPHuTmZjs5uQ0dPMrTsxuxyZBhfpMmzvzwIf3c+dOamlpdPLvPm7tk4+bVkZF3zcwsxo2Z0rt3f4SQQCA4c/bk02ePUlPf62jrenl5T5k8W0VFpUk+5prAnhNUbev2oPSM1G1b961ft/PJk8gnTyKpVCqRn4WLZ8bERi9c8POfh0O1NLXnzJ34MfMDQuj6tUiE0E9LVtecTLFYvGDRDBqNtmXznh3b9tNp9JWrFgqFQoTQwUN7Ll06E7R2+6qfN+jpGSxb8b/09E+3Nrp771ZZmWDL5j0/LQlMSIg5enQ/8fzuPVvPngseOiQg+NQV7x6+a9YuvXvvNrGIwWCEhB43N7e8Ef5w2tS54dcvL1w0w7dX339uPPbp6b9tx7pSQSlORd8DAAAgAElEQVRC6PyFkODTxwJGjd+4YdfMmfPv3P2H+OtAOggnqAKfX/z48YNRI8c7tHPS0dFdvGhVdnYmsSg+PiY9PfXnFes6d/LS1taZPWuBBlfz3LngujeekZFWVFQ4fNhoO1t7GxvbNYGb167dJpFI+CX8sDMnebyJHT08u3b1XrJ4lUcHz4LCfGIrDkd1/Lip7m4e3j18vby84+JfIIREItGNm1fHjJ40aOBwrga33w+DfXv1PfHXIcVr2baxHzRwOJPJ7OntjxBydHTx6elPp9N9evaWSCTpaSkIoVEjxx0+eLqnt5+7m0f3bj4+PXs/ffawsT/RhoBuLajC++S3CCEnJ1fioZqaWvv2ndIzUhFC8QkxDAajvXtHYhGFQnFz7RAb97zujZuammtqam3e+ou/Xz831w5OTq7ubh4IodjY5wghe3tHYjU6nR60dptiK2cnN8XPXA1NsUiEEHrzJkksFnf06KJY5ObaIfz6ZX4Jn+j0mptbEs+rqqoihCwtbYiHbDYHIVRaWkLsYJ9FPdq8Zc27928kEglCSEtLKVNd1heEE1SB+F+rqqqmeEZDg0v8IBCUVlZWEsNLBU1Nrbo3zmKxfvv10N/XLp49F3zkz33GxqaTJszw9+8nEJQihFRYVQ/26PT//q9SKBRFMQih/82f+tXKRYUFRDgVaxKoVU3SfvDQnmvXLs6cOb+jRxcDA8PDR37H5IAzhBNUgcVSQQhVisWKZ4qKC4kfdHR02Wz2hvW/fr4+jUqrV/vm5pazZy2YPGnW8+dPw69f3rg50MLSmvhbUF5eVvd2dHT1EEKLF600MTH7/Hl9fcM6tiCXy69cPTdi+JgB/T/dl4UIPA4gnKAKxNHXlNT3lpbWxPHM58+fGhgYIYRsbOwqKir09Q1NjE2JlTOzPmpy67HnTE9PTXwZ90PfQSoqKl5ePTp37tq3X9c3b5J6evvT6fTYuOft2jkRsVmxcoGPt3+fPgOqa8rUxJzFYiGEiI4xcZBZLpdzOJw6FlNZWVlRUaGr+2lGX7FY/PDRvbq/F6WCA0KgCibGphYWVsdPHPyY+UEgEOz6bZORkQmxqEP7Tp06eW3fvi4nJ5vPL7546cys2eOvX79M9Ff19PSjoh6/iIkiBm9VKinhb90WtP/Arg8fMzIy0k4FH5VIJE6Ormpqav5+/S5dOhN+/fKLmKg9e7dFRz8hglodDoczaeLME38dio+PEYvFd+/dXrJ0zq7fNtf9nTKZTHNzy/Drlz9mfuDzi7duD3J2cistLSkvL6/PB6YUsOcEVVu6JHD7zvXjJwy1sbb19++nqqqWlJRALNq0YdflK+eC1q94+TLezMzCz++HYcM+3Uhz7JgpR48dePrs4engq+pq6lW27OTkumjhz8eO/xF25iRCyKND5507DhC76Pk/Ltv12+YdOzdIpdI2NnZBv2xTHNGpDi9ggo2NXXDIsefPn6qqqjk6uCxevKpe73T1yo2/79sxafIIFRWVObMXubl5PH36cMKkYWfDrternUYH90ppFR79XSCXU52716PzyecXC4VCA4NPg7cVKxfQafR1QduVVmMz9jqKLygU9xyp17jNQrcWVG1t0PKFi2bcf/Avn1/818kj0dFPBg0aQXZRrQvsOVuFhuw5S/jbtgelp6fm5eVYmFuNHzeta1fvum8+cFDP6hYtW/ZLt67VLm2OlLTnhHC2Cg0I53fK+v8rir6lpamNw5WrjUhJ4YQDQkApjAyNyS6h2YMxJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgiuEWgUmmyqTwR9iZWEwaSqqjf/xwi+sVVDXpOdlVJBdRYuVm1Ghym38/RyEs1XQN2PDFxyURyqV6Zs1/qX8EM5WQVOPbmDGeng5l+xCWqBnN/LVuTQDc1ajtwxfGWtF4u7z099WOHfV1jZgIkodNgA1KswWv3pSpG3E7OivlO/iQThbl7cvBLH3ivMyRQwm1umUSKR0ev2m22x6qhp0N2/Ndp00lNQ+hLOVqiiTkl1CTfr163fhwgVi2ks8sTk0Zfc+4FRKK8VWxXq/JJYI2Ko0FgvrIpUNDggBgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACgCkIJwCYgnACHLm4uJBdAvkgnABHcXFxZJdAPggnAJiCcAKAKQgnAJiCcAKAKQgnAJiCcAKAKQgnAJiCcAKAKQgnAJiCcAKAKQgnAJiCcAKAKQgnAJiCcAKAKQgnAJiCcAKAKYpcLie7BgA+ad++PfEDhUJBCMnlciqVOnPmzOnTp5NdGglgzwkwYmNjQ6VSqVQqhUKhUChUKtXS0nLcuHFk10UOCCfAyJgxY1gsluIhnU4fPHgwm80mtSjSQDgBRoYOHWpmZqZ4aGJiMnLkSFIrIhOEE+BlxIgRxM6TTqcPGTJERUWF7IpIA+EEeBkxYgSx8zQ1NR0+fDjZ5ZAJwgmwM3LkSCaTOXDgQA6HQ3YtZIJTKS1HdERRamIZjUbNSa8gu5bvJZFI6XQa2VV8L30LtkQss2yn2rG3VgM2h3C2EKc2p7f10NTUZ+gYqcCvFBMUKirMEvHzxXH3CyeusqTUs58K4WwJTm1O7+Cna2LbqjuBOCvIFEeEfJyy1qpeW0E4m71nN4toDJptew2yCwE1SYkXlPPFnv21674JHBBq9lISBVoGrDqsCMikbcR6F1dar00gnM0enUHVMYRw4o6ry1DVoEsq67EJhLPZy0qtkFPILgLUQW6GUC6rxygSwgkApiCcAGAKwgkApiCcAGAKwgkApiCcAGAKwgkApiCcAGAKwgkApiCcAGAKwgkApiCcAGAKwgkApiCcAAspKe95YwaQXQVeIJwAC6/fvCS7BOxAOFujy1fOjRs/ZNCQXhs3B+bkZPv4etyOuEEsSkyMW7ps3qDBPuMnDtu3/9eysjLi+bVBy4PWrXj48N6gIb38+3jOXzg9KSlB0eD1G1fmzJv0Q/9uc+ZNOnsuWDH3zeChvufOnZ6/cLqPr0dJaQlC6PyF0KXL5g0c1HP4yD5B61Z8zPyAEDp67MCWrWuJSs6cPYUQKiwsWL9hJW/MgCHD/DZsWp2RkVaX93U74sa48UN8fD3mzJuUlZ3p4+tx6/Z1hFBI6Ikf+ndTrEa8UGTk3ZqLX/PL0qB1K/44uNvH1+PY8YM+vh4JCbGKRt69e+Pj60HUryQQzlYn6VXir7s2eXv7/XX8fM8efkHrVyCEqFQqQujDx4wlS+cIRcK9e46uW7s9OfntwkUzJBIJMf964su4f25dO7D/r/C/H7CYrE1b1hAN3rp9fcvWtXa29sEnL0+bOvfsueC9+3YQixgMxtVrF9q0abtt6+8cNic+PmbP3m2Ojq5BQduXL1tbVFS4YeMqhNDkSbN4ARMMDAz/vR01csRYqVS6cPHMmNjohQt+/vNwqJam9py5E2uNQXp66oaNq3x9+166GDFl8uyNm1YTZde8Vc3FJ6e8S055t2HdziGDRxoYGN66Ha7Y8O69W1yuprGRyff9NmoC4Wx1bt68qq2tM3nSLC5X08urR0cPT8WiW7fCGXTGurXbzc0tLS2tlyxe/fbd6weRd4ilFeXlPy0JNDYyodPpvr36ZmSklZeXI4SuXbvo4uK+YP5yLS3t9u4dJ0+cdfFiWFFRIXEnPw0N7v/mLvHo0JlOpzs4OB89EjZ2zGR3N4+OHp6jRo5LSkrgl/C/qjA+PiY9PfXnFes6d/LS1taZPWuBBlfz3Lngmt/XjZtXNTW1JoyfrqGu4dGh88D+w+ryadRcfHZ25to1W728emhqag0cMDwi4oZUKiU2/PfOP316DyBuVagkEM5WJznlXbt2TopdSo/uvopFiYmx9vaOXK4m8dDQ0MjY2DQu/gXx0MzcUjEFu5qaOkKotLREJpMlJMZ29OiiaMTdvaNMJlNs1dbOQbGIRqNlZn5Y8fP8AYO8fXw9fl61ECFUXFT4VYXxCTEMBqO9e0fiIYVCcXPtEBv3vOb39e7d67ZtHWi0T1NROzq5Enf4rGGTWou3MLdS3Kylf78hgjLBkyeRCKHk5HcfP2b0+2FwzSV9p1p2+qDlEQhK9fUNFQ8VUSQWvXr90sfX4/P1iwoLiB+Iru9XxGJxZWXlkT/3Hflz3xdb/X/kmEym4snIyLurAhePHTN55oz5Nja2UdFPli6bV2WFlZWVX5WhqVnLpOnFxUUmJv/doYytUvuNA2sv/rP7EWpqanX18r4dcd3Lq8fde7fsbO0tLOo3D219QThbHRZLRVL53yRwBYX5ip+1dXSdnd0mT5r1+fpcDU1UPRUVFQ6H09u/f48evp8/b2xk+u3KV69dcHZ2mzZ1LvFQIKh6qkgdHV02m71h/a+fP0mj1nJ3BnV1DZFYpHhYXlFe3ZpSmbQBxRM7z7XrlpeUljyIvNPvhyE11/P9IJytjomJ2du3rxQPI/9/SIkQsrG2vfnP364u7RU7ydTUZFNT85obtLGxKxWUurt92tFVVlZmZX3U1zf4ds2SEr6hgZHi4f37EdU1WFFRoa9vaGL8KSSZWR81ubXsOQ0NjZ88jZTJZETxsbHRikUMBlMkEkkkEqIzn56W0oDiEUKdO3fV0OCGhp5IS0vx8+1bcz3fD8acrU5XL++0tJTg08fkcvmzqMfx8TGKRSNGjJXJZHv37RAKhRkZaX8c3D1lWkByyruaG5w+dV5k5J1r4ZdkMll8fEzQuhWLlswSi8XfrtnGxu5Z1OMXMVESiYQ4ZYIQys7JQgiZmpoXFOQ/eHAnIyOtQ/tOnTp5bd++Licnm88vvnjpzKzZ469fv1xzGd7efvn5efv2/yqRSB4/fhB25qRikYODs1wuv37jCnEeJTjkWAOKJ0a/P/QddO78aa8uPT4fDigJhLPV6dG919Aho46fODh0uP+Fi6HTps0jThsghDTUNY4cDmWrsGfOHjdh0vCY2Oiflqy2s7WvuUFnZ7eDB07Fxb0YOtx/ydI5ZWWC9et2fn73eIUpU+Z07uS1avWi3n275ORkL1+21r6tw/IVP966fd2zczdnJ7fVa5YQZ1w3bdjl7e0XtH7FkGF+5y+E+Pn9MGwYr+YyOnp4zpzx46NH9/z7eG7YuOrzznk7e8fZsxYcPLjbx9cjaP2KqZPnKI4V1b14gpeXt0gk6u3fvw6f9PeCe6U0e78veTduZZuqDtZUTSKRpKYmt2ljRzxMepU4Z+7EQ38EK55pGYqLi4YO9w9cvcmnp38jNhsSeuLy5bMn/7pY5eGxmgVvej9lrTWDVdezLzDmbHXiE2IWLZ41ZPDIgFETCgvzd+/Z6ujoYmNjS3ZduIuJic7M+nD8xMFf1mxtQDIbAMLZ6ri7eSxetDL8+uUp00apqal7dPCcNWuBUk+mN5bg08dOnz5W5SILS+u9u/9U6qsvXT6PRqNNnTKncycvpb6QAnRrm736dmubL5FIJK6s5lANoqipqTV5RfUD3VrQYrFYrBoO1bQ8reDvLQDNE4QTAExBOAHAFIQTAExBOAHAFIQTAExBOAHAFIQTAExBOJs5OdIzUWkO194BpGOsgupzPR6Es5mjILFIWpJf9UVtAB/lJZKSQjFDpR5/RyGczZ6ZnWppoYTsKkAtSgoqLR3qd/UvhLN5Kysr02ubf+dsFtmFgFrcPZvdpZ92vTaBcDZLAoEAIfTw4cP+/fvn5mVPWm0Zsi2lMBs6tzji51We3ZU6aoEZW62WOcq+Al8Za2aKiop++uknAwODDRs25OXl6enpEc+XlUgjL+e/jSm1cVEvKaisrRncSSVSGr1+/5W/n0goolAoFCqFgij/oTb8aJuGDj0loczKUbVLfx2uLqO+m0M4m4djx449f/589+7deXl5mZmZrq6uVa4mk6K8TJFc2ux/pzNmzNi7d+/nc942gVWrVqWnp9NoNDabzWKxmEwmjUaTSCRsNnvt2rUNaJBCoegaM2mMBsYbvs+Jrw8fPly7dm348OE6OjoCgWDu3LkIIT09PcXe8ltUGjIwawnfeLR11jO25tR6p5PGNXfx2GXLluUVFqIv59ONiopqyjIUYMyJnZcvX6ampiKE9u3bhxDS1NRECM2bN69t27Zkl9Z0duzY0cTJRAi5u7t369btqyc1NDSauAwF6NbiIicnx8DAYP/+/Y8ePdq4caOpadWTjrcSjx8/7tSpU9PMo/U5Pp8/fvz4zMxM4qFMJgsPDzcwqHqOaWWDPSf5Xrx44e/v/+TJE4TQmDFjTpw40cqTiRBavHhxZSUJh7W4XO7o0aMVNy8yMDCYOnXqn38qd+qw6kA4ySEQCIKCglatWkV0XMPCwgYNGkT85yC7NCx4enoq7hfWxEaPHm1jYyOTyVRVVcPDw69evSoUCkeMGPHyZVPfexu6tU0qIiIiOjr6p59+ysjIePHiRe/evRV/pAE+Xrx4sWjRon///VfxTGpqamBgoIuLy5IlS5qsDNhzKp1UKg0PDy8rK5NKpdevX/fw8EAImZmZDRo0CJJZncePH8tkMrJe3d3d/fNkIoQsLS2J4UavXr0ePHjQNGXAnlNZSktLKyoq9PX1J06caG5uHhgYSNyPBNRF165dIyIiMJwIs6SkJDAwkMPhrFu3Ttkdb9hzNjKhUIgQOnHixKBBg0pLSxFCx48fX7duHSSzXkgcc9ZMQ0Nj165dPXv27Nq16+XLtdz47DvBnrPRvHnzZuvWrb169RozZkxqaqqlpSXZFQHlCgoKyszMDAoK0tfXV0b7EM7vIhKJTp8+LRAI5s2bFxsbK5fL3dzcyC6qJSDrPGd9RUVFBQYGjho1atKkSY3eOO5vHk/v378PDg5GCKWkpAgEgsGDByOEXF1dIZmNhazznPXl4eFx7do1gUAwatSoV69e1WGLeoBw1kNiYqJIJKqsrFy5ciVxWy57e/t58+aZmZmRXVpL07VrVzzHnFWaN2/e5s2b169fv3PnzkZsFrq1tRMIBGpqajNnzhQKhYcOHWIwGM3ihnmg6QUHB//5559BQUFeXo1wm0AIZ02uXLmyZ8+eXbt2OTg4FBYWamvX75vsoMEiIyO7dOmC/5jzW3w+PzAwUE1NLSgo6Dt3/s3vzStbZmbmunXrwsLCEEKGhoYhISEODg4IIUhmU1q6dGmzGHN+i8vl/vbbbz169PDy8rpy5cr3NAXh/CQiIoII5Nu3b11dXYcMGYIQ6tixI2SSFM1rzPmtPn36PHny5Pnz57Nnz87Ly2tYI626W1tZWfnkyZNu3bpFR0eHhoaOHTu2uhkGAGiYZ8+eBQYG8ni8iRMn1nfb1rjnFAgEUqlUJBL16NEjOjoaIdShQ4etW7dCMvERGRlJ4rW1jahjx47h4eElJSUBAQGvX7+u17ataM8pkUjodPqaNWvu3r178+ZNGo3WrDtOLRu219Y22Pv379esWdOhQ4eFCxfWcZNWsed8+PDh9OnTExMTEUIjRoy4c+cOMXcT2XWBajX3Mee3bGxsTp48aWBg4Ofn9/Dhw7ps0mL3nCUlJefOnTM2Nu7Tp8+1a9eMjIzc3d3JLgoAVFxcHBgYqKGhERQUVPO5opa253z//n1ERARC6NatW+Xl5Z06dUII9evXD5LZvLSYMee3NDU1d+/e3a1bN09Pz6tXr9awZosKZ1xcnOLCumHDhs2dO1dLS4vsokBDHDlyRCJpyTeA6du379OnT58+fXr48OHq1mlR4bSzswsJCfHx8SG7EPC9dHV1m+PlQfU1f/780NDQ6pa2nPeflZW1Y8cOsqsAjWPr1q1NP28tKWr4G9RywlleXh4XF0d2FaBxtOAxZ921nHAaGxsTM02CFqD5XlvbiFpOONlstrOzM9lVgMbR8s5zNkDLCWdWVtaGDRvIrgI0jtYz5qxBywknjDlbEhhztqhwwpizJYExZ4sKJ4w5WxIYc7aocMKYsyWBMWeLCieMOVsSGHO2qHDCmLMlgTFniwonjDlbEhhztqhwwpizJYExZ4sKJ4w5WxIYc7aocMKYsyWBMWeLCieMOVsSGHMihJp9t37hwoX37t2Ty+UUCkUmk1GpVGJWpOfPn5NdGmi4rVu3kl0C+Zr9nnP69On6+vpUKpVCodBoNAqFIpfLYcag5g7GnC0hnA4ODk5OTp8/o6OjM2bMGPIqAo0AxpwtIZwIoQkTJujo6CgempmZ+fr6kloR+F4w5mwh4XR2dlYcClJVVR07dizZFYHvBec5W0g4EUKTJk0idp7W1tZ+fn5klwO+171792DM2ULC6eTk5OrqqqKiAqPNlmHFihUw5qzldgyVIvnzf4ty04VlpdImrKohxOLKosJCA0MDsguphaYug0KlmFiznbpqkF0Ldvr27ctkMhFCBQUFWlpaVCpVIpEYGxvXMPNys1ZQUDBmzJgbN25UubSmbn1uhuj87x/cvHXsPDRZqs1idG5CdgG1o1GphTnC0hLp6W3pAYvMqDQK2RVhhEajZWZmEj9nZ2cTN4oeP3482XWRo9pwfnwvfHytYOwKm6atp1XQ1GcghPRMWKE7M0b/ZE52ORhxdnbOzs4mbqhBsLW19fb2JrUo0lQ95pRJ0YNLeb5jjJu8nlbEwILt2EX7/sV8sgvBCI/HMzIyUjxszbvNasOZ8bacqUKj0aHHpVzGNpxXUSVkV4ERNzc3e3t7xUN7e/uuXbuSWhGZqg5ncY7YyIrT5MW0OiwOVc9EpbSwJd9Oq77GjBmjq6tL7DZ5PB7Z5ZCp6nAKy2WSytZ+lqlpCIor4aP+XPv27du1a0eMNrt37052OWRq7RdhgO8lR+UCaVmJpEIglVQ2zl3S+/WcnJ9OG9ArIDmhrFEapNMpbDUaR4POUadRms9YDcIJGqK0UJKaVPb2RZmwQiYormSq0NS0WGJRY50M1/3Ba1FJBnqcwW+U5pgqNEGRSFwhVdVgsNgUWzdVSwc1ri7u//lxrw/gprRIcu9CflGuhK7CVNPV0LZhk11Rnej+/w+CwopXL8pjH2RzdWg9hupq6TPILawGEE5QD3fPFbyJKdW31jZ1VSW7lgZS02arabMRQiW55ed/z7Sw5/iN1iO7qKq1kGtrgbJJJfLj69KK+HRbLzOuYXNN5uc09Dk2nqYCIevw6hRhOY7H5CCcoHbCMtkfy5MN2xloGqmRXUsj0zRUtWhvfHxdahkfuxNaEE5QizK+NOy3Dw6+lixVfIdn34OhQm/bw+L8vix+Pl7fg4Fwglqc2Jhq6tLyL+Q0czP+a2Ma2VV8AcIJanL5ULZVB+NW8tUZWy+z83szya7iPxBOUK2XT0rKBRQVdSbZhTQRliq9UkaPuVtMdiGfQDhBtR5cytez1ia7iialb6P98GoB2VV8AuEEVYu7z9e14NKYret/CIVKMbTVjrqFxc5T6R/9yIAfDh/5vVGaSk5+5+PrERf3olFaAzWLi+RztPD9ZtK5K1u37RmtjJbVdDjxD1tHOEFzJCiWVAikrWe0+Tkmhy6XUwqzxWQXAuEEVUl9WcY1aAmXATWMup5aSmI52VU03rW1Uqn0zNlTx08cRAg5tHOeNHGms7Pbp9egM85fCD3wxy4mk+nk5LZieRBXg4sQkkgkR/7c9/jJg9zcbCcnt6GDR3l6diM2KSkt+eOP366FX+JyNT06dJ4+7X8GBoZfveKJvw4Hnz76686D7ewdaygsNTV585Y1796/0dTUCly16dCRvZYW1osXrQwJPXH8xMHwvx8Qq+XkZPPGDFgftKNrV2+EUGJi3PETB1+9SuRqanXx7D5xwgxVVVWE0JpfltJoNAMDo5DQEyt/Xrdj54axY6aMGztF8SEMHe4/dMioyZNmNdYHS4rsNBFdRYm7zWfPrz56diEr552RQRs3Z7/uXXjEvEFrNvXp4zujrLz4ZsRhFpPd1tZz8A+LNDR0EUIiUfmps4HvkqOMDNp06ThMebUhhJgcRlYq+eFstD3nwUN7Ll06E7R2+6qfN+jpGSxb8b/09FRi0d17t8rKBFs27/lpSWBCQszRo/uJ53fv2Xr2XPDQIQHBp6549/Bds3bp3Xu3idAuX/FjfkHezh0H/jfvp9y8nOU//yiRfHF11a3b148eO7B65caakymVSpet+J+Wts7pU1e2bt4bEnYiIyONwajlSpcPHzOWLJ0jFAn37jm6bu325OS3CxfNIApgMBjJKe+SU95tWLfTo4OnT8/et26HKzZ8ERNVWlri26vvd3yQWBDwJQyWsuZbfB57I/TCOlPjtj8vuvCD/+x7D0MuXfuVWESjMe48OEmhUINW3Fz6Y1hKWuyNfw8Ri8IubsgvyJg5ae/E0Vuyc5NfvYlUUnkIIQaLVlZC/tV8jbPn5Jfww86cXDB/eUcPT4RQ585dy8vLCgrzzc0tEUIcjur4cVOJNSMf3o2Lf4EQEolEN25eHTN60qCBwxFC/X4YnJAQe+KvQ949fB8/eZCUlHD86FliczMzi7AzJwsL/zvAHRMTvWXrLzNn/Ejs5WoQFf0kNzdn88bdenr6enr68/+3jDdmQM1T9SKEbt0KZ9AZ69Zu53I1EUJLFq8ePXbgg8g7Pb39KBRKdnbmgX1/qaioIIT69xsSfv3y23evbdu0RQjdvXvLvq0DUXazVsaXamsp6xtLT6MvWVu4Dxu4FCGkrqbdx3dG2IX1vt6T1NW0EUK62qZ+3pMRQmy2ets2nh8+vkII8UvyYhNuBQxdbWHmhBAa0Gfey1f3lVQeQojOpJeXkh/Oxtlzpqa8RwjZ//9OjE6nB63d5u7mQTx0dnJTrMnV0BSLRAihN2+SxGJxR48uikVurh2Sk9/xS/jv37/lcDiK/+J2tvarfl6vr/9ptuj0jNRVgYt8e/XlBZy/ep8AABQXSURBVEyotbD379+oqKhYWX2a4NPAwFBf36DWcCYmxtrbOxLJRAgZGhoZG5sSf1MQQhbmVkQyEUKOji6mpua3boUjhORy+d17t/39+9ftM8Mak02jM5Sy55TJZCnpcXa2nRXP2Fp7yOWylNQY4qGpSTvFIjZbQygSIIQKiz4ihAz0rRSLzD5brdHRGFR2k1xITKFQrK2tq1vaOH8dBYJShJAKS6Xq1/jsjjSKKUmJTf43f+pXKxcVFpSVCVjVNIUQ+m33FolEoq2tU90KX7RWVMhmf3E+QEWl9i8HCwSlr16/9PH1+Kow4gcmi/X580MGjTwZ/OesmfNfxERVVJT7+f1Ql8IwJ5fJxMJKBrvx8ymRiKXSyuu3Dly/deDz50vLCv//xyouFSwr5yOEWMz/fpVMphK/5F0plFSKm+IWB3K5PDk5ubqljRNOVVU1hFB5eT1mfNHR1UMILV600sTE7PPn9fUNORzViopy4jbV327Yp/cAe3vHHTs3eHh4tnfvWPOrqKtriMWiz5+pqKh6oC+V/ffL0NbRdXZ2++qgDldDs8oN/Xv3P3Dwt6joJ48e3/fq0kNDvSXcZEFVgy5ptDlHvsBkqrCYnA5u/Vwce33+vI52TbP1q3K4CCFxpVDxjFDUONMLVUkilqpqkD8PQeN0a9u0aUun02PjPt3pXS6XL/95/o0bV2vYxNTEnMViIYTc3TyIf5YW1hbmVhwOx76tg1AofP0miVgzPT11waIZ79+/JR729u8/oP/QHt17bdi4il9SyxwzRobGZWVlikNTHzM/5OXlEj8zGEyRSKQ4zpSelqLYysbaNjc329WlvaI2LU3t6kaSGuoaPb397t69FRFxw9+vX90+MNzpmbBk0saZretbxkZ2FcLSNtYdiH+W5i7q6jqa3JpucqOlaYwQSk2PIx5KJJVv3z9VUnnEN8t1TVh1WFG5Giecampq/n79Ll06E3798ouYqD17t0VHP2nXzqmGTTgczqSJM0/8dSg+PkYsFt+9d3vJ0jm7ftuMEPLw8DQxMTt4cPf9B/8+i3q867fNebk5FhZWn2++9Kc1dDp985Y1NRfWpUsPJpO5bcc6oVD49t3rTZsD1dQ+fV3YwcFZLpdfv3GFOI8SHHJMsdWIEWNlMtnefTuEQmFGRtofB3dPmRaQnPKuulfp128IccxWcSqouTO0YJXmC5TUeD//2QlJd59EX5bJZClpMSfDVv5xdK5EUtNJf02uvqW5642Ig7l5aZWVolNnViNlzqJXmiswtmwp4UQIzf9xmZubx46dGxYtnhUfHxP0y7ZaD1ryAib8tCQwOOTYwME9f9u9xdjIdPHiVcQYdfvWfTK5LHDNT0uXzVNhszdt/O2rW6mqqqquWb35yZPI8xdCa3gJNTW1Det/FVZUDBjkPXPWuB7de+nq6hOL2tk7zp614ODB3T6+HkHrV0ydPIfY5xM7wyOHQ9kq7Jmzx02YNDwmNvqnJavtbO2rexV3Nw86ne7v16/F3O/V3J5Tml8hV87cHVYWbgtnn0hJjfllS98/jv2vQiiYPHYbg1FLGEYPX2Nu6rhr/4SV6304bI1O7Qeh2g7sNRg/t8zKifxrMKq+BeCT8MLKSuTq3QK/kTB56ihXl/YL5i9vxDZfv0maPWfCiWPnTE3rfVeiS/vS+k8x0jLA7kK5m6dyRVKOul7zmFyvEZUVCpFI0H9KU9xLsuZbAMLle9/l3bs3kZF3N25aPZo3sQHJxJlbd25hRhHZVZCgMKPItQcWR/WafTcs+PSx06ePVbnIwtJ67+4/lfrqBw/tfhb12N+/35TJs5X6Qk1P35ylY0AvyS3T0K+6gxf5+Ez47QNVLqqsFFXXTeUNC3Rq12i39Iu4dzzi/okqF7FZahWiqofNY0eua2fnVeUiQUGFqjrVtA0W/YVm360tLy+v7uwInU5XXEiALWy7tQih4jxJ+IkcI4evr2omiMQVImHV5zMqRGVsVtWRZnM0GPRGe7MiUblIVPVvXyIR06t5ITZbvbq/HTmvcn1HaTfZodqG39m6WeBwOBwOvl87bNY09ehOnuovo/MNbHW/XcpislnVXAmggapYXxlYLA6L1Wi//bzkgjaubBxOohBgzAlq4txVQ8+QWpiBxZePlar4Y6mGhrxDL4y6WhBOUIteo/SMTKgFaY1zTyE8FX4o0dSW9hnfFEdo6w7CCWrXpb+Wtq40710+2YUoRX5KgTqnsuewOl2t3ZQgnKBOfEbotfNg577OFuQr8aLWJiYoKM9OyrZxYPqP1Se7lio0+wNCoMk4ddEwa8OJvJqf/pyvZaapqs1uppNNy+VIkF9RnFnM1ab6j9bTMcLxUDmEE9QPV4/eb7JhboYoLpL/6m6uhq6Kqo4qhUqhs+gMFh1RlHU93XeiyCmVIkmlSCKTycsLy/g5FW1c1f1G6RpZV/vNRBxAOEG96Zux/Hj6fjz9jDcVOenC/ExRWaFEwqDy88ifsa5KXH2mVCxT5dJ1DBn63bQs7Gv6eho+IJyg4czs2GZ2WFxM0yJVHU4qHdHhWFGTYKvSMe0LArJVnUBVDTq/ANMuSguT+0HI1W6Z970E36nqcOoYscTCpphDpZUTFEuMrNg0RrM85gmUrepwGpizGEzKu5jSJq+ndXl0NdfNG6PrxQBWqh1Y9p1g+OG14PWzlnzRFrkiTmc5eXEtHeCqfVC1mo7WDpxh9G9Y7vndaRraDBU1Zc3/3WjkSCavesI+rHDU6Znvy5kqVCcvDTt38ufCANiq5VSKzyh9YZksP1NUxid/Auya5eTknDlzZt68eWQXUgsqDTl0VtM1YlFw/zMCSFb7eU4VVaqpbTM4l0V/n5tbEdPWQ53sQgBoHPDXGwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwFTLCSeTyaRSqWlpaWQXAkBdxcbGampWez8Oilzecu5A9+jRox07dhgaGvJ4vG7dupFdDgDVOnfuXEhIiL6+/qpVq4yMjKpcp0WFk/D48eOQkJDU1NRRo0bxeDz8b9AAWo+srKyQkJDQ0NDBgwfzeDwrK6saVm6B4SR8/PgxNDQ0JCRkxIgRAQEBFhYWZFcEWrWnT5+Ghoa+efOGx+MFBATQ6bXfbKHFhlMhLCwsNDTU2NiYx+N17dqV7HJAq3P+/PmQkBBdXV0ej9ejR4+6b9jyw0l49OhRSEhIWlpaQEAAj8ejUOB+tUC5srOzQ0JCQkJCBg8eHBAQYG1tXd8WWks4CR8/fiQ+r1GjRgUEBJibm5NdEWiBnj17FhIS8vr1ax6Px+Px6tKDrVLrCqdCWFhYSEiIqalpQEAA9HVBYzl//nxoaKi2tjaPx/P29v7O1lppOAkPHz4MDQ1NS0sj/sKRXQ5ornJycoge2cCBAwMCAmxsbBql2VYdTsKHDx+Io9vEqRczMzOyKwLNRlRUVEhIyKtXr4hjGQwGoxEbh3D+hzj1YmZmxuPxvLy8yC4HYO38+fNhYWGampo8Hq9nz57KeAkI59eIvm56ejpxPorscgBecnJyiD/iAwYMaMQebJUgnFXLyMgICQkJCwuDvi4gREdHh4SEvHz5kujBMplMZb8ihLMWxJ9Jc3PzgIAA6Ou2ThcuXAgNDeVyuTwez8fHp8leF8JZJw8fPgwJCfnw4QOPxxs1ahTZ5YCmkJubS/xp7tevH4/HU2oPtkoQznpIT08PDQ0NCwsjOjampqZkVwSUIjo6OjQ0NCEhgTjuwGKxSCkDwtkQxEktCwsLHo/XpUsXsssBjebixYshISFcLjcgIKBXr17kFgPhbLjIyMiQkJDMzMyAgADo6zZreXl5xLnuvn378ni8Nm3akF0RgnA2grS0tLCwsLCwMOIyIxMTE7IrAvUQHR0dFhYWFxdH/PrI6sFWCcLZOORyOfGn19LSMiAgAPq6+Lt06VJISIiGhgYOPdgqQTgb2YMHD0JDQ6Gvi638/HzikEGfPn14PJ6trS3ZFVULwqkUaWlpoaGhZ8+eJQ73QV8XB8+fPw8NDY2NjSV6sCoqKmRXVAsIpxLJZDKir2tlZcXj8Tw9Pb9awcfHZ9u2bR4eHiQV2Fpcvnw5JCRETU2Nx+Ph2YOtEoSzKTx48CAkJCQ7OzsgIGDkyJH/1969BzV15XEAP7l5kMQEkCBVE7SUJZItM20VQ5WdAibyUBZolARf7U5nde3Lot3Zimy6uxaqu+MUOh3c7RTt1na2QWsgTrvB8bHtFLbqVEWQNWMtCELAklCB5CaEJOwf1424BXmp9+Ty+/x178m5Ob9k8h1OLjfnUo05OTlWq1UqlR48eFAikdBdIwP19PRQP9xNT0/X6XRyuZzuiiYHwvnwtLW1GQwGo9FIXWaUk5PDYrH8fr9cLjcYDHRXxyjUDLaxsZG6XAT/GeyoIJwPm8/nq6qqKi8v9/v9gUalUrl///7A7vAwav6mr7d7yNnvpalMTHF5BH8WERXNly8Wjdqhpqbm8OHDIpFIp9OpVKqHXuD9BOGkR3Jy8uDgYGCXzWZnZ2fr9XqEUHfboOlvnTEJojkyAYcHC5HdhSCIgV4POeDtvk6u3Sbj8W8vSmyz2ajrYDMyMrRabdDNYEc1xaWHwDSRJMlmswO7Pp+vtrZWJpNlpq77xty7/o3HEKRyTEKEUG+3p7qic802WWPTxcAM9sSJE0E6gx0V/OWkQWpqqsfj4fP5HA6HIAiCILhcLpvN5rC5GYq92h0xkMyJ6L7uuvzvHvPFEgbMYEcF4aSH0WgUi8UCgSAkJEQkEgkEglmzZtlaQ9osruTcR+iuLmgcKWvVFkaLwpk5AWTmq8KfRqP5aeN35+yRMgEd5QSreTFCe9cgU8MJN/nBiKvfN+J7KJiAYeR2+ifQLyhBOAHAFIQTAExBOAHAFIQTAExBOAHAFIQTAExBOAHAFIQTAExBOAHAFIQTAExBOAHAFIQTAExBOAHAFIQTAExBOAHAFIQziLW0XEtTJZ45U7dWm/nrLesQQkXFhUXFhYEOx49/nqZKJEkSIZSnUZuOfXbo40rVSmV2Tsqfdu+0223jDkGSZLF+R9bqX6zMeLrGdKTyQMVzv1qDELpiaU5TJV6xNAd6btyUt/+vZdR2b6+9pLS4YH12nkZdukd/40Yb1X7UaFiTn1FX/6VqpfK9in2vbd/8uzdeGTmc/s3flpQW3793KLhBOIMYl8tFCB36pFKn3fT6jt+P27mq6hBBEDXVpz768GjT5Ya/f/T+uEO8U/52y/fflZd9UPXpFx0d7SdPmalB78Hn821//TcNl85vL9x1sLJqdnjESy8/32ntQAjxeDySdB479lnRzt3P5mpXZeaev3Cut9dOHeh2u8+crVu27JnJvAdMBuEMYiwWCyG0NPHp/LUbFPGPj9tfKo3euOEFsUgskUQuTVx29eqVe/d3OBxffXVSq920SK6IiJC8/NIODoc77qJTTU0N7e3XdxW9laRcHhEheXFrYWhY+NGj/6AKdrvdBQXPq1WZMtmCtLR0oVB4+l/HqQPr6r9ECCUpkyfzHjAZhDPoyeMUE+0pv9NTLA51Oh337t/e3ur1euP/F3sWi6VQJIwfzssNXC538VNLA0c9+cSSS40XAh3iF91+Qh6Pp1ZlnTxppna//vp08vIUkWj01aJnIGaujDSj8CZ8v1fqL+3EURNOoUAYaBm5PRaHY2BoaChNddfdmcLDZwe2eTxeYDt7tabGdKTT2iGJiDx7rl5f/PakKmQ2CCeT+fy+6RweFhaOEBr03FmZ3kk6x+rs9d2+c4REEikQCEpLykY+yiZGX7ksNjZOoUgwm01xcfECgTApCea0d0A4GYXH5d3q+zGwGzhNOjVz585HCFkszfK4eOqOhv9pbgzh8xFCIbwQhJDLRVI9HQ6HzdZDbcfGyl0uV1TUXOl8GdVi7eoMD5s91iirsnINVYc6OtrVqiwOBz6Qd8B3TkZRKBIsluaWlmsIoW/Pn6VOsUzZnDlRCQlPVB6o6Oi8YbP1lJXvGXD0Uw9FRy8Ui8T/NJuGh4e9Xu/ev/xBLA6lHlqyWKlULt+3762bN7v7+m7VmI5sfXFTbe2xsUZZkZZht/ecPVe/Kit3OtUyD4STUfJytaoVmVu2bkhTJZrNpo3rX0AITWdR/6Kdu+MX/XzzlnX5uiyn05HyjJpq53K5ev0ei6V5hXrpug2/TE1ZOW+eNDDQntLylBT17pKiPI3aWG1Qq7M0moKxhhAKhUuWJC2IfjQmJnbKdTIS3I4BI6c+/SFiPv9nT4bSXciYyt/de6nxwocHDt/H5/R4PPm6rC2bX129Km+yx9ZV33wsQbgoUXwf68EHTPEBbbq7uzqtN4zVhoULY2BO+1MQzhmtqalh14jL/f7PJx/XUCdsH5BTp2srD1TExz/+xzf/PNl/88wEMK3FCC3T2ntcYSuRRD7MSqYAprWAyfBP4IwFZ2sBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEwBMQTgBwBSEEyMCMdvlmNbaBTONy+EVhjL2KjcIJ0YipSEDPw7RXUUwcfR5JfMmuoRS0IFwYkT+lKirlXTc8tJdSHC4er5fGisQihn7GWbsCwtS+dui6003b/3gobsQ3F27OGBtcaoKougu5AGCn4xhhxzwfV5pZbFYUQsEBBt+5XgXNofVb/d43H6+kEjf+Ajd5TxYEE5MdbW6bV0e1wBMce/CIliiMHbkfP4cGW8C3YMbhBMATMF3TgAwBeEEAFMQTgAwBeEEAFMQTgAwBeEEAFP/BSqHYnk7/hoJAAAAAElFTkSuQmCC", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeStyles\n", - "\n", - "display(Image(agent.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "977173cf-8f8d-416e-a122-aebff115f29a", - "metadata": {}, - "source": [ - "We can now invoke the graph exactly as before:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6c800a77-2359-4011-ab0c-dad22ed333b1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================\u001b[1m Human Message \u001b[0m=================================\n", - "\n", - "Which genre on average has the longest tracks?\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "Available tables: Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " sql_db_schema (call_qxKtYiHgf93AiTDin9ez5wFp)\n", - " Call ID: call_qxKtYiHgf93AiTDin9ez5wFp\n", - " Args:\n", - " table_names: Genre,Track\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: sql_db_schema\n", - "\n", - "\n", - "CREATE TABLE \"Genre\" (\n", - "\t\"GenreId\" INTEGER NOT NULL, \n", - "\t\"Name\" NVARCHAR(120), \n", - "\tPRIMARY KEY (\"GenreId\")\n", - ")\n", - "\n", - "/*\n", - "3 rows from Genre table:\n", - "GenreId\tName\n", - "1\tRock\n", - "2\tJazz\n", - "3\tMetal\n", - "*/\n", - "\n", - "\n", - "CREATE TABLE \"Track\" (\n", - "\t\"TrackId\" INTEGER NOT NULL, \n", - "\t\"Name\" NVARCHAR(200) NOT NULL, \n", - "\t\"AlbumId\" INTEGER, \n", - "\t\"MediaTypeId\" INTEGER NOT NULL, \n", - "\t\"GenreId\" INTEGER, \n", - "\t\"Composer\" NVARCHAR(220), \n", - "\t\"Milliseconds\" INTEGER NOT NULL, \n", - "\t\"Bytes\" INTEGER, \n", - "\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n", - "\tPRIMARY KEY (\"TrackId\"), \n", - "\tFOREIGN KEY(\"MediaTypeId\") REFERENCES \"MediaType\" (\"MediaTypeId\"), \n", - "\tFOREIGN KEY(\"GenreId\") REFERENCES \"Genre\" (\"GenreId\"), \n", - "\tFOREIGN KEY(\"AlbumId\") REFERENCES \"Album\" (\"AlbumId\")\n", - ")\n", - "\n", - "/*\n", - "3 rows from Track table:\n", - "TrackId\tName\tAlbumId\tMediaTypeId\tGenreId\tComposer\tMilliseconds\tBytes\tUnitPrice\n", - "1\tFor Those About To Rock (We Salute You)\t1\t1\t1\tAngus Young, Malcolm Young, Brian Johnson\t343719\t11170334\t0.99\n", - "2\tBalls to the Wall\t2\t2\t1\tNone\t342562\t5510424\t0.99\n", - "3\tFast As a Shark\t3\t2\t1\tF. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman\t230619\t3990994\t0.99\n", - "*/\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " sql_db_query (call_RPN3GABMfb6DTaFTLlwnZxVN)\n", - " Call ID: call_RPN3GABMfb6DTaFTLlwnZxVN\n", - " Args:\n", - " query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgTrackLength\n", - "FROM Track\n", - "JOIN Genre ON Track.GenreId = Genre.GenreId\n", - "GROUP BY Genre.GenreId\n", - "ORDER BY AvgTrackLength DESC\n", - "LIMIT 1;\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "Tool Calls:\n", - " sql_db_query (call_PR4s8ymiF3ZQLaoZADXtdqcl)\n", - " Call ID: call_PR4s8ymiF3ZQLaoZADXtdqcl\n", - " Args:\n", - " query: SELECT Genre.Name, AVG(Track.Milliseconds) AS AvgTrackLength\n", - "FROM Track\n", - "JOIN Genre ON Track.GenreId = Genre.GenreId\n", - "GROUP BY Genre.GenreId\n", - "ORDER BY AvgTrackLength DESC\n", - "LIMIT 1;\n", - "=================================\u001b[1m Tool Message \u001b[0m=================================\n", - "Name: sql_db_query\n", - "\n", - "[('Sci Fi & Fantasy', 2911783.0384615385)]\n", - "==================================\u001b[1m Ai Message \u001b[0m==================================\n", - "\n", - "The genre with the longest tracks on average is \"Sci Fi & Fantasy,\" with an average track length of approximately 2,911,783 milliseconds.\n" - ] - } - ], - "source": [ - "question = \"Which genre on average has the longest tracks?\"\n", - "\n", - "for step in agent.stream(\n", - " {\"messages\": [{\"role\": \"user\", \"content\": question}]},\n", - " stream_mode=\"values\",\n", - "):\n", - " step[\"messages\"][-1].pretty_print()" - ] - }, - { - "cell_type": "markdown", - "id": "fc3d8130-15c3-4c22-bb17-eb82ad41586e", - "metadata": {}, - "source": [ - "!!! tip\n", - "\n", - " See [LangSmith trace](https://smith.langchain.com/public/94b8c9ac-12f7-4692-8706-836a1f30f1ea/r) for the above run." - ] - }, - { - "cell_type": "markdown", - "id": "838b3acf-3186-4aa1-b211-cf136e95c39d", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "Check out [this guide](https://docs.smith.langchain.com/evaluation/how_to_guides/langgraph) for evaluating LangGraph applications, including SQL agents like this one, using LangSmith." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 020c6e28a..a66966905 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -109,6 +109,7 @@ nav: - Agent architectures: concepts/agentic_concepts.md - Guides: + - guides/index.md - LangGraph APIs: - Graph API: - Overview: concepts/low_level.md @@ -268,6 +269,7 @@ nav: - Environment variables: cloud/reference/env_var.md - Examples: + - examples/index.md - Template applications: concepts/template_applications.md # TODO: make tutorial - Agentic RAG: tutorials/rag/langgraph_agentic_rag.md - Agent Supervisor: tutorials/multi_agent/agent_supervisor.md @@ -286,6 +288,7 @@ nav: - Implement generative UI with LangGraph: cloud/how-tos/generative_ui_react.md - Additional resources: + - additional-resources/index.md - agents/prebuilt.md # NOTE: prebuilt.md is auto-generated by `make build-prebuilt` - LangGraph Academy course: https://academy.langchain.com/courses/intro-to-langgraph - Case studies: adopters.md From fb1c0ae9f9072363e1c934d259549ed131f1bcd1 Mon Sep 17 00:00:00 2001 From: Jake Broekhuizen Date: Tue, 8 Jul 2025 09:55:15 -0700 Subject: [PATCH 29/31] docs: Updating mcp_tools_node fn & referencing runtime graph rebuild docs (#5328) Fix: Updating mcp_tools_node fn & referencing runtime graph rebuild docs --- docs/docs/concepts/server-mcp.md | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/docs/docs/concepts/server-mcp.md b/docs/docs/concepts/server-mcp.md index 0d40bfb2e..b66eaada3 100644 --- a/docs/docs/concepts/server-mcp.md +++ b/docs/docs/concepts/server-mcp.md @@ -209,7 +209,7 @@ To make user-scoped tools available to your LangGraph Platform deployment, start ```python from langchain_mcp_adapters.client import MultiServerMCPClient -def get_mcp_tools_node(state, config): +def mcp_tools_node(state, config): user = config["configurable"].get("langgraph_auth_user") # e.g., user["github_token"], user["email"], etc. @@ -223,14 +223,19 @@ def get_mcp_tools_node(state, config): } }) tools = await client.get_tools() # (3) - return {"tools": tools} - + + # Your tool-calling logic here + + tool_messages = ... + return {"messages": tool_messages} ``` 1. MCP only supports adding headers to requests made to `streamable_http` and `sse` `transport` servers. 2. Your MCP server URL. 3. Get available tools from your MCP server. +_This can also be done by [rebuilding your graph at runtime](https://langchain-ai.github.io/langgraph/cloud/deployment/graph_rebuild/) to have a different configuration for a new run_ + ## Session behavior The current LangGraph MCP implementation does not support sessions. Each `/mcp` request is stateless and independent. From 9cb6365914d11ca33909d1d3c5f56565c91fc16b Mon Sep 17 00:00:00 2001 From: Michael Li Date: Wed, 9 Jul 2025 04:58:04 +1000 Subject: [PATCH 30/31] docs: update file paths to make the examples more robust (#5382) * cli: update file paths to make the examples more robust * fix: fix the prompt path --- libs/cli/examples/graphs_reqs_a/graphs_submod/agent.py | 2 +- libs/cli/examples/graphs_reqs_b/graphs_submod/agent.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/libs/cli/examples/graphs_reqs_a/graphs_submod/agent.py b/libs/cli/examples/graphs_reqs_a/graphs_submod/agent.py index 5ab615871..1073110cb 100644 --- a/libs/cli/examples/graphs_reqs_a/graphs_submod/agent.py +++ b/libs/cli/examples/graphs_reqs_a/graphs_submod/agent.py @@ -17,7 +17,7 @@ model_oai = ChatOpenAI(temperature=0) model_anth = model_anth.bind_tools(tools) model_oai = model_oai.bind_tools(tools) -prompt = open("prompt.txt").read() +prompt = open(Path(__file__).parent.parent / "prompt.txt").read() subprompt = open(Path(__file__).parent / "subprompt.txt").read() diff --git a/libs/cli/examples/graphs_reqs_b/graphs_submod/agent.py b/libs/cli/examples/graphs_reqs_b/graphs_submod/agent.py index 5ab615871..1073110cb 100644 --- a/libs/cli/examples/graphs_reqs_b/graphs_submod/agent.py +++ b/libs/cli/examples/graphs_reqs_b/graphs_submod/agent.py @@ -17,7 +17,7 @@ model_oai = ChatOpenAI(temperature=0) model_anth = model_anth.bind_tools(tools) model_oai = model_oai.bind_tools(tools) -prompt = open("prompt.txt").read() +prompt = open(Path(__file__).parent.parent / "prompt.txt").read() subprompt = open(Path(__file__).parent / "subprompt.txt").read() From 0d8a8c5847703e78b88b3b2e3fb224a4d3adff63 Mon Sep 17 00:00:00 2001 From: William FH <13333726+hinthornw@users.noreply.github.com> Date: Tue, 8 Jul 2025 12:41:47 -0700 Subject: [PATCH 31/31] feat: [CLI] Add arg to retain build deps (setuptools, pip, wheel) (#5404) --- docs/docs/cloud/reference/cli.md | 1 + libs/cli/examples/langgraph.json | 1 + libs/cli/langgraph_cli/config.py | 124 ++++++++++++++++++---- libs/cli/pyproject.toml | 2 +- libs/cli/schemas/schema.json | 36 ++++++- libs/cli/schemas/schema.v0.json | 36 ++++++- libs/cli/schemas/version.schema.json | 46 ++++++++ libs/cli/tests/unit_tests/cli/test_cli.py | 7 +- libs/cli/tests/unit_tests/test_config.py | 57 +++++++++- libs/cli/uv.lock | 2 +- 10 files changed, 280 insertions(+), 32 deletions(-) create mode 100644 libs/cli/schemas/version.schema.json diff --git a/docs/docs/cloud/reference/cli.md b/docs/docs/cloud/reference/cli.md index b43be5485..2181f59e2 100644 --- a/docs/docs/cloud/reference/cli.md +++ b/docs/docs/cloud/reference/cli.md @@ -51,6 +51,7 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema]( | `node_version` | Specify `node_version: 20` to use LangGraph.js. | | `pip_config_file` | Path to `pip` config file. | | `pip_installer` | _(Added in v0.3)_ Optional. Python package installer selector. It can be set to `"auto"`, `"pip"`, or `"uv"`. From version 0.3 onward the default strategy is to run `uv pip`, which typically delivers faster builds while remaining a drop-in replacement. In the uncommon situation where `uv` cannot handle your dependency graph or the structure of your `pyproject.toml`, specify `"pip"` here to revert to the earlier behaviour. | + | `keep_pkg_tools` | _(Added in v0.3.4)_ Optional. Control whether to retain Python packaging tools (`pip`, `setuptools`, `wheel`) in the final image. Accepted values:
  • true : Keep all three tools (skip uninstall).
  • false / omitted : Uninstall all three tools (default behaviour).
  • list[str] : Names of tools to retain. Each value must be one of "pip", "setuptools", "wheel".
. By default, all three tools are uninstalled. | | `dockerfile_lines` | Array of additional lines to add to Dockerfile following the import from parent image. | | `checkpointer` | Configuration for the checkpointer. Contains a `ttl` field which is an object with the following keys:
  • `strategy`: How to handle expired checkpoints (e.g., `"delete"`).
  • `sweep_interval_minutes`: How often to check for expired checkpoints (integer).
  • `default_ttl`: Default time-to-live for checkpoints in **minutes** (integer). Defines how long checkpoints are kept before the specified strategy is applied.
| | `http` | HTTP server configuration with the following fields:
  • `app`: Path to custom Starlette/FastAPI app (e.g., `"./src/agent/webapp.py:app"`). See [custom routes guide](../../how-tos/http/custom_routes.md).
  • `cors`: CORS configuration with fields for `allow_origins`, `allow_methods`, `allow_headers`, etc.
  • `configurable_headers`: Define which request headers to exclude or include as a run's configurable values.
  • `disable_assistants`: Disable `/assistants` routes
  • `disable_mcp`: Disable `/mcp` routes
  • `disable_meta`: Disable `/ok`, `/info`, `/metrics`, and `/docs` routes
  • `disable_runs`: Disable `/runs` routes
  • `disable_store`: Disable `/store` routes
  • `disable_threads`: Disable `/threads` routes
  • `disable_ui`: Disable `/ui` routes
  • `disable_webhooks`: Disable webhooks calls on run completion in all routes
  • `mount_prefix`: Prefix for mounted routes (e.g., "/my-deployment/api")
| diff --git a/libs/cli/examples/langgraph.json b/libs/cli/examples/langgraph.json index 51eb14be9..0d8563e55 100644 --- a/libs/cli/examples/langgraph.json +++ b/libs/cli/examples/langgraph.json @@ -8,6 +8,7 @@ "scikit-learn", "./graphs" ], + "keep_pkg_tools": false, "graphs": { "agent": "./graphs/agent.py:graph", "storm": "./graphs/storm.py:graph" diff --git a/libs/cli/langgraph_cli/config.py b/libs/cli/langgraph_cli/config.py index cd0356350..ce1c6e8b9 100644 --- a/libs/cli/langgraph_cli/config.py +++ b/libs/cli/langgraph_cli/config.py @@ -338,7 +338,7 @@ class HttpConfig(TypedDict, total=False): Default is False. """ disable_meta: bool - """Optional. If True, all meta endpoints (/ok, /info, /metrics, /docs) are disabled. + """Optional. If True, all meta endpoints (/openapi.json, /info, /metrics, /docs) are disabled. Default is False. """ @@ -471,21 +471,61 @@ class Config(TypedDict, total=False): """Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file. """ + keep_pkg_tools: Optional[Union[bool, list[str]]] + """Optional. Control whether to retain Python packaging tools in the final image. + + Allowed tools are: "pip", "setuptools", "wheel". + You can also set to true to include all packaging tools. + """ -PIP_CLEANUP_LINES = """# -- Ensure user deps didn't inadvertently overwrite langgraph-api + +_BUILD_TOOLS = ("pip", "setuptools", "wheel") + + +def _get_pip_cleanup_lines( + install_cmd: str, + to_uninstall: Optional[tuple[str]], + pip_installer: Literal["uv", "pip"], +) -> str: + commands = [ + f"""# -- Ensure user deps didn't inadvertently overwrite langgraph-api RUN mkdir -p /api/langgraph_api /api/langgraph_runtime /api/langgraph_license && \ - touch /api/langgraph_api/__init__.py /api/langgraph_runtime/__init__.py /api/langgraph_license/__init__.py +touch /api/langgraph_api/__init__.py /api/langgraph_runtime/__init__.py /api/langgraph_license/__init__.py RUN PYTHONDONTWRITEBYTECODE=1 {install_cmd} --no-cache-dir --no-deps -e /api # -- End of ensuring user deps didn't inadvertently overwrite langgraph-api -- -# -- Removing pip from the final image ~<:===~~~ -- -RUN pip uninstall -y pip setuptools wheel && \ - rm -rf /usr/local/lib/python*/site-packages/pip* /usr/local/lib/python*/site-packages/setuptools* /usr/local/lib/python*/site-packages/wheel* && \ - find /usr/local/bin -name "pip*" -delete || true -# pip removal for wolfi -RUN rm -rf /usr/lib/python*/site-packages/pip* /usr/lib/python*/site-packages/setuptools* /usr/lib/python*/site-packages/wheel* && \ - find /usr/bin -name "pip*" -delete || true -{uv_removal} -# -- End of pip removal --""" +# -- Removing build deps from the final image ~<:===~~~ --""" + ] + if to_uninstall: + for pack in to_uninstall: + if pack not in _BUILD_TOOLS: + raise ValueError( + f"Invalid build tool: {pack}; must be one of {', '.join(_BUILD_TOOLS)}" + ) + packs_str = " ".join(sorted(to_uninstall)) + commands.append(f"RUN pip uninstall -y {packs_str}") + # Ensure the directories are removed entirely + packages_rm = " ".join( + f"/usr/local/lib/python*/site-packages/{pack}*" for pack in to_uninstall + ) + if "pip" in to_uninstall: + packages_rm += ' && find /usr/local/bin -name "pip*" -delete || true' + commands.append(f"RUN rm -rf {packages_rm}") + wolfi_packages_rm = " ".join( + f"/usr/lib/python*/site-packages/{pack}*" for pack in to_uninstall + ) + if "pip" in to_uninstall: + wolfi_packages_rm += ' && find /usr/bin -name "pip*" -delete || true' + commands.append(f"RUN rm -rf {wolfi_packages_rm}") + if pip_installer == "uv": + commands.append( + f"RUN uv pip uninstall --system {packs_str} && rm /usr/bin/uv /usr/bin/uvx" + ) + else: + if pip_installer == "uv": + commands.append( + "RUN rm /usr/bin/uv /usr/bin/uvx\n# -- End of build deps removal --" + ) + return "\n".join(commands) def _parse_version(version_str: str) -> tuple[int, int]: @@ -563,6 +603,7 @@ def validate_config(config: Config) -> Config: "checkpointer": config.get("checkpointer"), "ui": config.get("ui"), "ui_config": config.get("ui_config"), + "keep_pkg_tools": config.get("keep_pkg_tools"), } if config.get("node_version"): @@ -635,6 +676,22 @@ def validate_config(config: Config) -> Config: f"Invalid http.app format: '{http_conf['app']}'. " "Must be in format './path/to/file.py:attribute_name'" ) + if keep_pkg_tools := config.get("keep_pkg_tools"): + if isinstance(keep_pkg_tools, list): + for tool in keep_pkg_tools: + if tool not in _BUILD_TOOLS: + raise ValueError( + f"Invalid keep_pkg_tools: '{tool}'. " + "Must be one of 'pip', 'setuptools', 'wheel'." + ) + elif keep_pkg_tools is True: + pass + else: + raise ValueError( + f"Invalid keep_pkg_tools: '{keep_pkg_tools}'. " + "Must be bool or list[str] (with values" + " 'pip', 'setuptools', and/or 'wheel')." + ) return config @@ -1128,6 +1185,27 @@ def _image_supports_uv(base_image: str) -> bool: return version >= min_uv +def get_build_tools_to_uninstall(config: Config) -> tuple[str]: + keep_pkg_tools = config.get("keep_pkg_tools") + if not keep_pkg_tools: + return _BUILD_TOOLS + if keep_pkg_tools is True: + return () + expected = _BUILD_TOOLS + if isinstance(keep_pkg_tools, list): + for tool in keep_pkg_tools: + if tool not in expected: + raise ValueError( + f"Invalid build tool to uninstall: {tool}. Expected one of {expected}" + ) + return tuple(sorted(set(_BUILD_TOOLS) - set(keep_pkg_tools))) + else: + raise ValueError( + f"Invalid value for keep_pkg_tools: {keep_pkg_tools}." + " Expected True or a list containing any of {expected}." + ) + + def python_config_to_docker( config_path: pathlib.Path, config: Config, @@ -1135,20 +1213,18 @@ def python_config_to_docker( ) -> tuple[str, dict[str, str]]: """Generate a Dockerfile from the configuration.""" pip_installer = config.get("pip_installer", "auto") - + build_tools_to_uninstall = get_build_tools_to_uninstall(config) + if pip_installer == "auto": + if _image_supports_uv(base_image): + pip_installer = "uv" + else: + pip_installer = "pip" if pip_installer == "uv": install_cmd = "uv pip install --system" - uv_removal = "RUN uv pip uninstall --system pip setuptools wheel && rm /usr/bin/uv /usr/bin/uvx" elif pip_installer == "pip": install_cmd = "pip install" - uv_removal = "" else: - if _image_supports_uv(base_image): - install_cmd = "uv pip install --system" - uv_removal = "RUN uv pip uninstall --system pip setuptools wheel && rm /usr/bin/uv /usr/bin/uvx" - else: - install_cmd = "pip install" - uv_removal = "" + raise ValueError(f"Invalid pip_installer: {pip_installer}") # configure pip pip_install = f"PYTHONDONTWRITEBYTECODE=1 {install_cmd} --no-cache-dir -c /api/constraints.txt" @@ -1297,7 +1373,11 @@ ADD {relpath} /deps/{name} js_inst_str, "", # Add pip cleanup after all installations are complete - PIP_CLEANUP_LINES.format(install_cmd=install_cmd, uv_removal=uv_removal), + _get_pip_cleanup_lines( + install_cmd=install_cmd, + to_uninstall=build_tools_to_uninstall, + pip_installer=pip_installer, + ), "", f"WORKDIR {local_deps.working_dir}" if local_deps.working_dir else "", ] diff --git a/libs/cli/pyproject.toml b/libs/cli/pyproject.toml index 1a781e6f2..8c959ccab 100644 --- a/libs/cli/pyproject.toml +++ b/libs/cli/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "hatchling.build" [project] name = "langgraph-cli" -version = "0.3.3" +version = "0.3.4" description = "CLI for interacting with LangGraph API" authors = [] requires-python = ">=3.9" diff --git a/libs/cli/schemas/schema.json b/libs/cli/schemas/schema.json index c069f7e9e..19d8893a4 100644 --- a/libs/cli/schemas/schema.json +++ b/libs/cli/schemas/schema.json @@ -134,6 +134,23 @@ ], "description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n" }, + "keep_pkg_tools": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "array", + "items": { + "type": "string" + } + }, + { + "type": "null" + } + ], + "description": "Optional. Control whether to retain Python packaging tools in the final image.\n\nYou can also set to true to include all packaging tools.\n" + }, "pip_installer": { "anyOf": [ { @@ -298,6 +315,23 @@ ], "description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n" }, + "keep_pkg_tools": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "array", + "items": { + "type": "string" + } + }, + { + "type": "null" + } + ], + "description": "Optional. Control whether to retain Python packaging tools in the final image.\n\nYou can also set to true to include all packaging tools.\n" + }, "pip_installer": { "anyOf": [ { @@ -505,7 +539,7 @@ }, "disable_meta": { "type": "boolean", - "description": "Optional. If True, all meta endpoints (/ok, /info, /metrics, /docs) are disabled.\n\nDefault is False.\n" + "description": "Optional. If True, all meta endpoints (/openapi.json, /info, /metrics, /docs) are disabled.\n\nDefault is False.\n" }, "disable_runs": { "type": "boolean", diff --git a/libs/cli/schemas/schema.v0.json b/libs/cli/schemas/schema.v0.json index c069f7e9e..19d8893a4 100644 --- a/libs/cli/schemas/schema.v0.json +++ b/libs/cli/schemas/schema.v0.json @@ -134,6 +134,23 @@ ], "description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n" }, + "keep_pkg_tools": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "array", + "items": { + "type": "string" + } + }, + { + "type": "null" + } + ], + "description": "Optional. Control whether to retain Python packaging tools in the final image.\n\nYou can also set to true to include all packaging tools.\n" + }, "pip_installer": { "anyOf": [ { @@ -298,6 +315,23 @@ ], "description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n" }, + "keep_pkg_tools": { + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "array", + "items": { + "type": "string" + } + }, + { + "type": "null" + } + ], + "description": "Optional. Control whether to retain Python packaging tools in the final image.\n\nYou can also set to true to include all packaging tools.\n" + }, "pip_installer": { "anyOf": [ { @@ -505,7 +539,7 @@ }, "disable_meta": { "type": "boolean", - "description": "Optional. If True, all meta endpoints (/ok, /info, /metrics, /docs) are disabled.\n\nDefault is False.\n" + "description": "Optional. If True, all meta endpoints (/openapi.json, /info, /metrics, /docs) are disabled.\n\nDefault is False.\n" }, "disable_runs": { "type": "boolean", diff --git a/libs/cli/schemas/version.schema.json b/libs/cli/schemas/version.schema.json new file mode 100644 index 000000000..041b130c1 --- /dev/null +++ b/libs/cli/schemas/version.schema.json @@ -0,0 +1,46 @@ +{ + "$id": "https://github.com/langchain-ai/langgraph/libs/cli/schemas/version.schema.json", + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "LangGraph Platform configuration (when building via the langgraph-cli).", + "type": "object", + "oneOf": [ + { + "allOf": [ + { + "oneOf": [ + { + "properties": { + "version": { + "type": "string", + "maxLength": 0 + } + }, + "required": ["version"] + }, + { + "not": { + "required": ["version"] + } + } + ] + }, + { + "$ref": "https://raw.githubusercontent.com/langchain-ai/langgraph/main/libs/cli/schemas/schema.json" + } + ] + }, + { + "allOf": [ + { + "properties": { + "version": { "const": "v0" } + }, + "required": ["version"] + }, + { + "$ref": "https://raw.githubusercontent.com/langchain-ai/langgraph/main/libs/cli/schemas/schema.v0.json" + } + ] + } + ] +} diff --git a/libs/cli/tests/unit_tests/cli/test_cli.py b/libs/cli/tests/unit_tests/cli/test_cli.py index 268f44e00..e6d0a17b4 100644 --- a/libs/cli/tests/unit_tests/cli/test_cli.py +++ b/libs/cli/tests/unit_tests/cli/test_cli.py @@ -10,13 +10,14 @@ from pathlib import Path from click.testing import CliRunner from langgraph_cli.cli import cli, prepare_args_and_stdin -from langgraph_cli.config import PIP_CLEANUP_LINES, Config, validate_config +from langgraph_cli.config import Config, _get_pip_cleanup_lines, validate_config from langgraph_cli.docker import DEFAULT_POSTGRES_URI, DockerCapabilities, Version from langgraph_cli.util import clean_empty_lines -FORMATTED_CLEANUP_LINES = PIP_CLEANUP_LINES.format( +FORMATTED_CLEANUP_LINES = _get_pip_cleanup_lines( install_cmd="uv pip install --system", - uv_removal="RUN uv pip uninstall --system pip setuptools wheel && rm /usr/bin/uv /usr/bin/uvx", + to_uninstall=("pip", "setuptools", "wheel"), + pip_installer="uv", ) DEFAULT_DOCKER_CAPABILITIES = DockerCapabilities( version_docker=Version(26, 1, 1), diff --git a/libs/cli/tests/unit_tests/test_config.py b/libs/cli/tests/unit_tests/test_config.py index 3971b60e2..691c1e888 100644 --- a/libs/cli/tests/unit_tests/test_config.py +++ b/libs/cli/tests/unit_tests/test_config.py @@ -9,7 +9,8 @@ import click import pytest from langgraph_cli.config import ( - PIP_CLEANUP_LINES, + _BUILD_TOOLS, + _get_pip_cleanup_lines, config_to_compose, config_to_docker, docker_tag, @@ -18,9 +19,10 @@ from langgraph_cli.config import ( ) from langgraph_cli.util import clean_empty_lines -FORMATTED_CLEANUP_LINES = PIP_CLEANUP_LINES.format( +FORMATTED_CLEANUP_LINES = _get_pip_cleanup_lines( install_cmd="uv pip install --system", - uv_removal="RUN uv pip uninstall --system pip setuptools wheel && rm /usr/bin/uv /usr/bin/uvx", + to_uninstall=("pip", "setuptools", "wheel"), + pip_installer="uv", ) PATH_TO_CONFIG = pathlib.Path(__file__).parent / "test_config.json" @@ -51,6 +53,7 @@ def test_validate_config(): "http": None, "ui": None, "ui_config": None, + "keep_pkg_tools": None, **expected_config, } assert actual_config == expected_config @@ -77,6 +80,7 @@ def test_validate_config(): "http": None, "ui": None, "ui_config": None, + "keep_pkg_tools": None, } actual_config = validate_config(expected_config) assert actual_config == expected_config @@ -925,6 +929,53 @@ def test_config_to_docker_pip_installer(): assert "uv pip install --system" in docker_default +def test_config_retain_build_tools(): + graphs = {"agent": "./graphs/agent.py:graph"} + base_config = { + "python_version": "3.11", + "dependencies": ["."], + "graphs": graphs, + } + config_true = validate_config( + {**copy.deepcopy(base_config), "keep_pkg_tools": True} + ) + docker_true, _ = config_to_docker( + PATH_TO_CONFIG, config_true, "langchain/langgraph-api:0.2.47" + ) + assert not any( + "/usr/local/lib/python*/site-packages/" + pckg + "*" in docker_true + for pckg in _BUILD_TOOLS + ) + assert "RUN pip uninstall -y pip setuptools wheel" not in docker_true + config_false = validate_config( + {**copy.deepcopy(base_config), "keep_pkg_tools": False} + ) + docker_false, _ = config_to_docker( + PATH_TO_CONFIG, config_false, "langchain/langgraph-api:0.2.47" + ) + assert all( + "/usr/local/lib/python*/site-packages/" + pckg + "*" in docker_false + for pckg in _BUILD_TOOLS + ) + assert "RUN pip uninstall -y pip setuptools wheel" in docker_false + config_list = validate_config( + {**copy.deepcopy(base_config), "keep_pkg_tools": ["pip", "setuptools"]} + ) + docker_list, _ = config_to_docker( + PATH_TO_CONFIG, config_list, "langchain/langgraph-api:0.2.47" + ) + assert all( + "/usr/local/lib/python*/site-packages/" + pckg + "*" in docker_list + for pckg in ("wheel",) + ) + assert not any( + "/usr/local/lib/python*/site-packages/" + pckg + "*" in docker_list + for pckg in ("pip", "setuptools") + ) + assert "RUN pip uninstall -y wheel" in docker_list + assert "RUN pip uninstall -y pip setuptools" not in docker_list + + # config_to_compose def test_config_to_compose_simple_config(): graphs = {"agent": "./agent.py:graph"} diff --git a/libs/cli/uv.lock b/libs/cli/uv.lock index a6a0d0270..b312f2e06 100644 --- a/libs/cli/uv.lock +++ b/libs/cli/uv.lock @@ -522,7 +522,7 @@ wheels = [ [[package]] name = "langgraph-cli" -version = "0.3.3" +version = "0.3.4" source = { editable = "." } dependencies = [ { name = "click", version = "8.1.8", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
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